# Phase 5: Optional Enhancements
**Status**: 🔵 Not Started
**Estimated Duration**: 4-6 weeks (can be implemented incrementally)
**Prerequisites**: Phases 1-4 Complete
**Note**: These are optional enhancements to ALL phases (1-4, 6). Can be implemented incrementally based on needs.
---
## Quick Reference
**What you'll build**: Optional enhancements across ALL implementation phases, adding advanced features for development, production, and research
**Tasks**: 200+ optional enhancements across 11 modules
**Phase 4 Enhancements** (World Generation):
- Module 5.1: Reproducibility & Benchmarking (8 tasks)
- Module 5.2: Extended Material System (12 tasks)
- Module 5.3: Environmental Effects (15 tasks)
- Module 5.4: Advanced Dynamics (10 tasks)
- Module 5.5: Automation & Utilities (8 tasks)
**Cross-Phase Enhancements**:
- Module 5.6: Phase 1 Enhancements - Setup & Dev Experience (22 tasks)
- Module 5.7: Phase 2 Enhancements - Infrastructure & Reliability (28 tasks)
- Module 5.8: Phase 3 Enhancements - Advanced Control & Multi-Robot (27 tasks)
- Module 5.9: Phase 6 Enhancements - Testing & Quality Assurance (18 tasks)
- Module 5.10: Automated Testing Pipelines & AI Reasoning (25 tasks)
**Advanced MCP Protocol Features**:
- Module 5.11: Prompts, Resources, Sampling & Extended Thinking (30 tasks) ⭐ NEW
**Success criteria**: Enhanced tools provide research-grade reproducibility, realistic environmental conditions, and advanced control while maintaining 100% backward compatibility
**Key deliverables**:
**Phase 4 (World Generation)**:
- ✅ Reproducible random seeds, benchmark worlds
- ✅ Extended material library (15+ materials)
- ✅ Weather and atmospheric effects
- ✅ Advanced wind, animations, trigger zones
**Phase 1 (Setup)**:
- ✅ Pre-commit hooks, dev containers
- ✅ CI/CD pipelines, health checks
- ✅ Multi-version ROS2 support
**Phase 2 (Infrastructure)**:
- ✅ Debug/mock modes, connection pooling
- ✅ Monitoring, metrics, circuit breakers
- ✅ Message batching, retry strategies
**Phase 3 (Control)**:
- ✅ Multi-robot coordination, swarm control
- ✅ Visual debugging, RViz integration
- ✅ Nav2 integration, sensor fusion
**Phase 6 (Testing)**:
- ✅ Visual test reports, benchmarking
- ✅ Snapshot testing, chaos engineering
- ✅ Performance regression detection
---
## Overview
**Phase 5** provides **optional enhancements** across **ALL implementation phases** (1, 2, 3, 4, and 6). These add advanced features for development experience, production reliability, research capabilities, and quality assurance.
**Important:** ALL enhancements are optional. Core phases work perfectly without them. Implement based on your specific needs:
- **Modules 5.1-5.5**: Phase 4 enhancements (world generation)
- **Module 5.6**: Phase 1 enhancements (setup & tooling)
- **Module 5.7**: Phase 2 enhancements (infrastructure)
- **Module 5.8**: Phase 3 enhancements (control & robotics)
- **Module 5.9**: Phase 6 enhancements (testing & QA)
- **Module 5.10**: Automated testing pipelines with AI reasoning
- **Module 5.11**: Advanced MCP protocol features (prompts, resources, sampling, extended thinking) ⭐ NEW
All enhancements:
- ✅ Use `Optional[]` type hints
- ✅ Provide sensible defaults
- ✅ Maintain 100% backward compatibility
- ✅ Work with `OperationResult` pattern
- ✅ Work with `ResultFilter` token efficiency
**Note:** This is an *enhancement* phase. Phase 4 tools work perfectly without these additions. Implement based on your specific needs.
---
## Priority Levels
### 🔴 HIGH PRIORITY (Critical for Research)
**Estimated Duration**: 1-1.5 weeks
Must-have features for serious robotics research and benchmarking:
1. Reproducible random seeds
2. Extended material library
3. Fog and atmospheric effects
4. Advanced wind system
5. Astronomical day/night cycle
6. Batch operations
7. Benchmark world generation
### 🟡 MEDIUM PRIORITY (Quality-of-Life)
**Estimated Duration**: 1-1.5 weeks
Significant improvements for testing and development:
8. Advanced obstacle course patterns
9. Heightmap multi-texture blending
10. Shadow quality controls
11. Animation system
12. Trigger zones
### 🟢 LOW PRIORITY (Future Enhancements)
**Estimated Duration**: 1-2 weeks
Nice-to-have features for advanced scenarios:
13. AI-assisted world generation
14. Recording and playback
15. Seasonal variations
16. Advanced acoustics
17. Digital twin integration
---
## Module 4.1B: Reproducibility & Benchmarking (Optional)
**Goal**: Enable reproducible research and standardized testing
### Tasks (0/8)
#### High Priority
- [ ] **Enhancement**: Add `seed` parameter to `create_obstacle_course`
- [ ] **Enhancement**: Add `seed` parameter to `create_procedural_terrain`
- [ ] **Enhancement**: Add `seed` parameter to `place_object_grid`
- [ ] **Enhancement**: Add `export_metadata` to obstacle course
- [ ] **Tool**: `create_benchmark_world` - Standardized test environments
#### Medium Priority
- [ ] **Enhancement**: Add `seed` to all random generation tools
- [ ] **Enhancement**: Export world specifications to JSON
- [ ] **Tool**: `compare_world_states` - Diff two world configurations
### Implementation: Reproducible Seeds
**Pattern for all procedural generation:**
```python
async def create_obstacle_course(
num_obstacles: int = 10,
area_size: float = 20.0,
obstacle_types: List[str] = ["box", "cylinder"],
min_distance: float = 1.0,
# NEW: Reproducibility
seed: Optional[int] = None,
) -> OperationResult:
"""
Create random obstacle course.
Args:
seed: Random seed for reproducible generation.
If None, uses current timestamp.
Same seed always generates identical layouts.
Example:
# Reproducible for benchmarking
>>> create_obstacle_course(num_obstacles=10, seed=42)
>>> # Always generates same layout
# Different each time
>>> create_obstacle_course(num_obstacles=10)
"""
import random
if seed is not None:
random.seed(seed)
# Existing implementation uses random.choice(), random.uniform(), etc.
# These will now be reproducible when seed is set
```
### Implementation: Benchmark Worlds
```python
@mcp_tool(
name="create_benchmark_world",
description="Create standardized benchmark world for testing"
)
async def create_benchmark_world(
benchmark_type: str, # "nav2_standard", "slam_office", "warehouse"
difficulty: str = "medium", # "easy", "medium", "hard", "expert"
# Reproducibility
seed: Optional[int] = None,
# Variations
include_dynamic_obstacles: bool = False,
dynamic_obstacle_count: int = 3,
# Export
export_ground_truth: bool = True, # Save reference map
export_path: Optional[str] = None,
) -> OperationResult:
"""
Generate standardized benchmark world.
Benchmark Types:
- nav2_standard: Standard Nav2 obstacle course
- slam_office: Office environment for SLAM testing
- warehouse: Warehouse with shelves and narrow aisles
- outdoor: Outdoor terrain with varied surfaces
Args:
benchmark_type: Type of benchmark world
difficulty: Challenge level
seed: Random seed (required for reproducibility)
include_dynamic_obstacles: Add moving obstacles
export_ground_truth: Save occupancy grid for comparison
Returns:
OperationResult with world metadata and file paths
"""
if seed is None and export_ground_truth:
return failure_result(
"Seed required when exporting ground truth",
suggestion="Provide seed parameter for reproducible benchmark"
)
# Load benchmark template
template = BENCHMARK_TEMPLATES[benchmark_type]
# Apply difficulty settings
difficulty_params = DIFFICULTY_SETTINGS[difficulty]
# Generate world with seed
world_data = template.generate(
seed=seed,
**difficulty_params
)
# Export ground truth
if export_ground_truth:
save_ground_truth_map(world_data, export_path)
return success_result({
'world_name': f"{benchmark_type}_{difficulty}_seed{seed}",
'benchmark_type': benchmark_type,
'difficulty': difficulty,
'seed': seed,
'ground_truth_path': export_path,
'metadata': world_data.metadata,
})
```
**Success Criteria:**
- [ ] Same seed generates identical worlds across runs
- [ ] Benchmark worlds match published specifications
- [ ] Ground truth maps export correctly
- [ ] Metadata includes all generation parameters
---
## Module 5.2: Extended Material System
**Goal**: Provide realistic material properties for diverse environments
### Tasks (0/12)
#### High Priority
- [ ] **Enhancement**: Extend material library to 15+ materials
- [ ] **Enhancement**: Add `rolling_friction` to `set_surface_type`
- [ ] **Enhancement**: Add `wetness` property
- [ ] **Enhancement**: Add PBR material properties
#### Medium Priority
- [ ] **Enhancement**: Add `particle_effects` system
- [ ] **Enhancement**: Add `contact_sound` support
- [ ] **Enhancement**: Add seasonal variants
- [ ] **Tool**: `create_custom_material` - User-defined materials
#### Low Priority
- [ ] **Enhancement**: Material wear over time
- [ ] **Enhancement**: Temperature-dependent properties
- [ ] **Tool**: `blend_materials` - Transition zones
- [ ] **Tool**: `import_material_library` - Load material packs
### Extended Material Library
**New materials to add:**
```python
EXTENDED_MATERIALS = {
# Existing: grass, concrete, ice
# NEW OUTDOOR MATERIALS
'wet_grass': {
'friction': 0.5,
'rolling_friction': 0.08,
'restitution': 0.1,
'wetness': 0.8,
'color': (0.15, 0.7, 0.15, 1.0),
'particle_effects': 'splash',
'contact_sound': 'wet_grass_squelch',
},
'sand': {
'friction': 0.6,
'rolling_friction': 0.08,
'restitution': 0.05,
'color': (0.9, 0.8, 0.6, 1.0),
'particle_effects': 'sand_kick',
'contact_sound': 'sand_crunch',
},
'gravel': {
'friction': 0.7,
'rolling_friction': 0.05,
'restitution': 0.2,
'color': (0.5, 0.5, 0.4, 1.0),
'particle_effects': 'dust',
'contact_sound': 'gravel_crunch',
},
'mud': {
'friction': 0.4,
'rolling_friction': 0.15,
'restitution': 0.01,
'wetness': 0.9,
'color': (0.3, 0.25, 0.15, 1.0),
'particle_effects': 'mud_splash',
'contact_sound': 'mud_squelch',
},
'snow': {
'friction': 0.3,
'rolling_friction': 0.1,
'restitution': 0.05,
'color': (0.95, 0.95, 0.98, 1.0),
'particle_effects': 'snow_puff',
'contact_sound': 'snow_crunch',
'temperature': -5.0, # Celsius
},
# NEW INDOOR MATERIALS
'asphalt': {
'friction': 1.0,
'rolling_friction': 0.01,
'restitution': 0.01,
'color': (0.2, 0.2, 0.2, 1.0),
'roughness': 0.8,
},
'wood_floor': {
'friction': 0.6,
'rolling_friction': 0.03,
'restitution': 0.2,
'color': (0.6, 0.4, 0.2, 1.0),
'contact_sound': 'wood_creak',
'roughness': 0.7,
},
'tile': {
'friction': 0.5,
'rolling_friction': 0.02,
'restitution': 0.1,
'color': (0.9, 0.9, 0.85, 1.0),
'roughness': 0.2,
'metallic': 0.0,
},
'rubber_mat': {
'friction': 1.5,
'rolling_friction': 0.02,
'restitution': 0.7,
'color': (0.2, 0.2, 0.2, 1.0),
'roughness': 0.9,
},
'metal_plate': {
'friction': 0.4,
'rolling_friction': 0.01,
'restitution': 0.2,
'color': (0.7, 0.7, 0.7, 1.0),
'roughness': 0.3,
'metallic': 1.0,
'contact_sound': 'metal_clang',
},
# PLANETARY MATERIALS
'mars_soil': {
'friction': 0.65,
'rolling_friction': 0.06,
'restitution': 0.05,
'color': (0.8, 0.3, 0.1, 1.0),
'particle_effects': 'red_dust',
},
'lunar_regolith': {
'friction': 0.7,
'rolling_friction': 0.08,
'restitution': 0.05,
'color': (0.5, 0.5, 0.5, 1.0),
'particle_effects': 'moon_dust',
},
}
```
### Enhanced Surface Type Tool
```python
async def set_surface_type(
surface_name: str,
material: str,
# Existing parameters
friction: Optional[float] = None,
restitution: Optional[float] = None,
# NEW: Advanced friction models
rolling_friction: Optional[float] = None, # Critical for wheeled robots
spin_friction: Optional[float] = None,
# NEW: Environmental properties
wetness: float = 0.0, # 0.0 = dry, 1.0 = soaked
temperature: Optional[float] = None, # Celsius
# NEW: Effects
particle_effects: Optional[str] = None, # "dust", "splash", "snow"
contact_sound: Optional[str] = None,
# NEW: Visual
surface_roughness: float = 0.0, # PBR roughness
) -> OperationResult:
"""
Set terrain surface material with advanced properties.
Args:
rolling_friction: Friction for rolling objects (wheels)
Lower = less resistance
Typical: 0.01 (asphalt) to 0.15 (mud)
wetness: Surface moisture (affects friction and appearance)
particle_effects: Visual effects on contact
Example:
# Wet grass reduces traction
>>> set_surface_type(
... surface_name="ground",
... material="grass",
... wetness=0.8, # After rain
... rolling_friction=0.08 # Higher resistance
... )
"""
```
**Success Criteria:**
- [ ] All 15+ materials implement consistent physics
- [ ] Rolling friction accurately affects wheeled robots
- [ ] Wetness property modifies friction appropriately
- [ ] Material properties validated against real-world data
---
## Module 5.3: Environmental Effects
**Goal**: Simulate weather and atmospheric conditions for sensor testing
### Tasks (0/15)
#### High Priority
- [ ] **Enhancement**: Add fog to `set_ambient_light`
- [ ] **Enhancement**: Astronomical accuracy to `set_day_night_cycle`
- [ ] **Tool**: `add_environment_effects` - Weather system
- [ ] **Enhancement**: Add turbulence to `set_wind`
#### Medium Priority
- [ ] **Enhancement**: Volumetric lighting for directional lights
- [ ] **Enhancement**: Shadow quality controls
- [ ] **Enhancement**: Color temperature for lights
- [ ] **Tool**: `set_weather_conditions` - Unified weather presets
#### Low Priority
- [ ] **Enhancement**: Cloud simulation
- [ ] **Enhancement**: Aurora effects
- [ ] **Tool**: `create_lighting_preset` - Custom presets
- [ ] **Tool**: `animate_environment` - Time-lapse effects
### Implementation: Fog System
```python
async def set_ambient_light(
color: Dict[str, float],
intensity: float,
# NEW: Fog system
fog_enabled: bool = False,
fog_density: float = 0.01,
fog_color: Optional[Dict[str, float]] = None,
fog_start: float = 10.0, # Meters
fog_end: float = 100.0,
) -> OperationResult:
"""
Set ambient lighting with optional fog.
Args:
fog_enabled: Enable distance fog
fog_density: Fog thickness (0.001 = light, 0.1 = dense)
fog_start: Distance where fog begins
fog_end: Distance where fog is opaque
Example:
# Heavy fog for sensor testing
>>> set_ambient_light(
... color={'r': 0.7, 'g': 0.7, 'b': 0.7, 'a': 1.0},
... intensity=0.4,
... fog_enabled=True,
... fog_density=0.05, # Dense fog
... fog_start=5.0,
... fog_end=50.0
... )
"""
```
### Implementation: Astronomical Day/Night
```python
async def set_day_night_cycle(
cycle_duration: float = 60.0,
start_time: str = "sunrise",
enabled: bool = True,
# NEW: Astronomical accuracy
time_of_day: Optional[float] = None, # 0-24 hours
latitude: float = 0.0, # -90 to 90
day_of_year: int = 172, # 1-365 (172 = summer solstice)
# NEW: Celestial
include_moon: bool = False,
moon_phase: float = 0.5, # 0 = new, 0.5 = full
star_field: bool = False,
# NEW: Atmosphere
atmospheric_scattering: bool = False,
cloud_cover: float = 0.0, # 0-1
) -> OperationResult:
"""
Set day/night cycle with astronomical accuracy.
Args:
latitude: Geographic latitude (affects sun angle)
Examples: 0 (equator), 40.7 (NYC), 64 (Alaska)
day_of_year: Day 1-365 (affects sun path)
172 = June 21 (summer solstice)
355 = December 21 (winter solstice)
atmospheric_scattering: Realistic sky colors
Example:
# Simulate winter in Alaska (low sun angle)
>>> set_day_night_cycle(
... latitude=64.0,
... day_of_year=355, # December 21
... atmospheric_scattering=True,
... )
# Test solar panels at equator
>>> set_day_night_cycle(
... latitude=0.0,
... time_of_day=12.0, # Solar noon
... )
"""
```
### Implementation: Weather Effects
```python
@mcp_tool(
name="add_environment_effects",
description="Add weather and atmospheric effects"
)
async def add_environment_effects(
effect_type: str, # "rain", "snow", "fog", "sandstorm", "dust"
intensity: float = 0.5,
duration: Optional[float] = None, # Seconds (None = continuous)
# Particle system
particle_count: int = 1000,
wind_direction: Optional[Dict[str, float]] = None,
# Sensor impact
affects_cameras: bool = True,
affects_lidar: bool = True,
visibility_reduction: float = 0.5, # 0-1
) -> OperationResult:
"""
Add environmental effects for sensor testing.
Effect Types:
- rain: Falling rain particles, wet surfaces
- snow: Falling snow, accumulation
- fog: Reduce visibility
- sandstorm: Dense particle cloud
- dust: Light airborne particles
Example:
# Test vision in rain
>>> add_environment_effects(
... effect_type="rain",
... intensity=0.7,
... affects_cameras=True,
... visibility_reduction=0.3
... )
# Mars dust storm
>>> add_environment_effects(
... effect_type="dust",
... intensity=0.8,
... particle_count=5000,
... wind_direction={'x': 1, 'y': 0, 'z': 0}
... )
"""
```
**Success Criteria:**
- [ ] Fog reduces camera and LiDAR range realistically
- [ ] Astronomical calculations accurate to within 1 degree
- [ ] Weather effects impact sensor readings
- [ ] Performance remains acceptable with particle effects
---
## Module 5.4: Advanced Dynamics
**Goal**: Enhanced physics simulation for complex scenarios
### Tasks (0/10)
#### High Priority
- [ ] **Enhancement**: Wind turbulence and gusts
- [ ] **Tool**: `batch_world_updates` - Atomic operations
#### Medium Priority
- [ ] **Enhancement**: Advanced force application
- [ ] **Tool**: `create_animated_object` - Moving obstacles
- [ ] **Tool**: `create_trigger_zone` - Interactive zones
- [ ] **Enhancement**: Sinusoidal forces for vibration testing
#### Low Priority
- [ ] **Tool**: `create_physics_constraint` - Joints/springs
- [ ] **Tool**: `enable_soft_body` - Deformable objects
- [ ] **Enhancement**: Fluid simulation
- [ ] **Tool**: `create_particle_emitter` - Custom particles
### Implementation: Advanced Wind
```python
async def set_wind(
direction: Dict[str, float],
strength: float,
# NEW: Turbulence
turbulence: float = 0.0, # 0-1
# NEW: Gusts
gusts_enabled: bool = False,
gust_frequency: float = 5.0, # Seconds between gusts
gust_strength_multiplier: float = 2.0,
# NEW: Altitude effects
altitude_gradient: float = 0.0, # Wind increase per meter
# NEW: Selective application
affected_models: Optional[List[str]] = None,
) -> OperationResult:
"""
Set wind with turbulence and gusts.
Args:
turbulence: Random wind variation (0-1)
gusts_enabled: Periodic strong wind bursts
gust_frequency: Seconds between gusts
altitude_gradient: Wind strengthens with height
Example: 0.1 = +10% wind per meter up
Example:
# Drone testing in gusty conditions
>>> set_wind(
... direction={'x': 1, 'y': 0, 'z': 0},
... strength=5.0, # m/s
... turbulence=0.3,
... gusts_enabled=True,
... gust_frequency=10.0,
... altitude_gradient=0.05 # Wind increases with altitude
... )
"""
```
### Implementation: Batch Operations
```python
@mcp_tool(
name="batch_world_updates",
description="Execute multiple world operations atomically"
)
async def batch_world_updates(
updates: List[Dict[str, Any]],
atomic: bool = True, # All-or-nothing
optimize_order: bool = True,
) -> OperationResult:
"""
Batch multiple world updates for performance.
Args:
updates: List of operations with params
atomic: If True, rollback all if any fails
optimize_order: Reorder for efficiency
Example:
>>> batch_world_updates([
... {
... 'action': 'spawn_model',
... 'params': {'model_name': 'robot1', ...}
... },
... {
... 'action': 'set_lighting',
... 'params': {'preset': 'sunset'}
... },
... {
... 'action': 'modify_terrain',
... 'params': {'material': 'sand'}
... },
... ])
"""
```
**Success Criteria:**
- [ ] Wind turbulence creates realistic disturbances
- [ ] Gusts affect flying objects appropriately
- [ ] Batch operations improve performance by >30%
- [ ] Atomic transactions rollback correctly on failure
---
## Module 5.5: Automation & Utilities
**Goal**: Tools for efficient world management
### Tasks (0/8)
#### Medium Priority
- [ ] **Tool**: `create_world_from_template` - Template system
- [ ] **Tool**: `export_world_specification` - Save config
- [ ] **Tool**: `import_world_specification` - Load config
- [ ] **Tool**: `world_state_snapshot` - Save/restore state
#### Low Priority
- [ ] **Tool**: `generate_world_from_description` - AI-assisted
- [ ] **Tool**: `optimize_world_performance` - Auto-optimize
- [ ] **Tool**: `validate_world_consistency` - Sanity checks
- [ ] **Tool**: `world_diff` - Compare two worlds
---
## Implementation Strategy
### Phase 5A: High Priority (Week 1-2)
**Note:** These enhance Phase 4 tools with research-critical features
**Focus:** Research-critical features
1. **Reproducibility** (3 days)
- Add `seed` to all procedural tools
- Implement `create_benchmark_world`
- Add metadata export
2. **Materials** (2-3 days)
- Extend to 15+ materials
- Add `rolling_friction`
- Add `wetness` property
3. **Environment** (2-3 days)
- Fog system
- Astronomical day/night
- Weather effects
4. **Wind** (1-2 days)
- Turbulence
- Gusts
### Phase 5B: Medium Priority (Week 3)
**Focus:** Quality-of-life
1. **Advanced Obstacle Courses** (2 days)
- Pattern types (maze, grid, circular)
- Difficulty presets
2. **Rendering** (2 days)
- Shadow quality
- Volumetric lighting
3. **Dynamics** (1-2 days)
- Animation system
- Trigger zones
### Phase 5C: Low Priority (Week 4)
**Focus:** Future features
1. **Automation**
- Batch operations
- World templates
2. **Advanced** (if time permits)
- Recording/playback
- AI generation
---
## Backward Compatibility Guarantee
**All enhancements MUST:**
✅ Use `Optional[]` type hints
✅ Provide sensible defaults
✅ Not change existing behavior
✅ Work with `OperationResult`
✅ Work with `ResultFilter`
**Example pattern:**
```python
async def enhanced_tool(
# Existing params (unchanged)
required_param: str,
existing_optional: float = 1.0,
# NEW params (all optional)
new_param: Optional[float] = None,
new_feature: bool = False,
) -> OperationResult:
"""Enhanced tool with new optional parameters."""
# Validation for new params only
if new_param is not None:
if not (0.0 <= new_param <= 1.0):
return failure_result("new_param must be 0-1")
# Existing code continues to work
# New features only activate if params provided
```
---
## Testing Requirements
### For Each Enhancement
```python
def test_backward_compatibility():
"""Tool works without new parameters"""
result = tool(required_params_only)
assert result.success
def test_new_parameter_validation():
"""New parameter validates correctly"""
result = tool(required_params, new_param=invalid)
assert not result.success
def test_new_parameter_functionality():
"""New parameter has intended effect"""
result1 = tool(required_params)
result2 = tool(required_params, new_param=value)
assert result1 != result2 # Different behavior
def test_seed_reproducibility():
"""Same seed produces same result"""
r1 = tool(params, seed=42)
r2 = tool(params, seed=42)
assert r1.data == r2.data # Identical
```
---
## Success Criteria
### Phase 5A (High Priority) ✅
**Reproducibility:**
- [ ] All procedural tools accept `seed` parameter
- [ ] Same seed generates identical worlds (100% reproducible)
- [ ] Benchmark worlds generate correctly
- [ ] Metadata export includes all generation parameters
**Materials:**
- [ ] 15+ materials implemented
- [ ] Rolling friction accurate for wheeled robots
- [ ] Wetness affects friction appropriately
- [ ] PBR properties render correctly
**Environment:**
- [ ] Fog reduces visibility realistically
- [ ] Astronomical calculations accurate to 1 degree
- [ ] Weather effects impact sensor readings
- [ ] Performance acceptable (>30 FPS with effects)
**Wind:**
- [ ] Turbulence creates realistic variation
- [ ] Gusts affect objects appropriately
- [ ] Altitude gradient works correctly
### Phase 5B (Medium Priority) ✅
- [ ] Maze pattern generates solvable mazes
- [ ] Difficulty levels behave distinctly
- [ ] Shadow quality settings work
- [ ] Animations smooth (>30 FPS)
- [ ] Trigger zones detect entry/exit accurately
### Phase 5C (Low Priority) ✅
- [ ] Batch operations improve performance >30%
- [ ] World templates save/load correctly
- [ ] Recording captures state accurately
---
## Performance Targets
| Enhancement | Target Performance |
|-------------|-------------------|
| Fog rendering | >60 FPS (moderate density) |
| Weather particles | >30 FPS (1000 particles) |
| Wind simulation | <1ms per frame |
| Batch operations | 30-50% faster than sequential |
| Seed generation | <100ms overhead |
| Material switching | <50ms |
---
## Module 5.6: Phase 1 Enhancements (Setup & Dev Experience)
**Goal**: Enhance development environment, tooling, and onboarding experience
**Note**: These enhance Phase 1 (Project Setup). Improve developer productivity and project professionalism.
### Tasks (0/22)
#### High Priority
- [ ] **Pre-commit Hooks** - Auto-format, lint, type-check on commit
- [ ] **Development Container** - VSCode devcontainer with ROS2+Gazebo
- [ ] **Makefile/Taskfile** - Common commands (`make test`, `make lint`, etc.)
- [ ] **Multi-Version ROS2 Support** - Support Humble, Iron, Jazzy
- [ ] **CI/CD Pipeline Templates** - GitHub Actions, GitLab CI
- [ ] **Development Health Checks** - Validate environment setup
- [ ] **GitHub/GitLab Templates** - Issue/PR templates
#### Medium Priority
- [ ] **IDE Configuration Templates** - VSCode, PyCharm settings
- [ ] **Quick-Start Bootstrap Script** - Interactive setup wizard
- [ ] **Alternative Build Systems** - Poetry, Conda support
- [ ] **Security Scanning** - Dependabot, vulnerability scanning
- [ ] **Build Caching Strategy** - Faster CI/CD
- [ ] **Dependency Visualization** - Generate dependency graphs
- [ ] **Documentation Site** - MkDocs/Sphinx with GitHub Pages
- [ ] **AI Assistant Integration** - Claude Code, Copilot optimization
#### Low Priority
- [ ] **Changelog Automation** - Auto-generate from commits
- [ ] **Compiled Dependencies** - Prefer pre-compiled wheels
- [ ] **Import Optimization** - Lazy imports for faster startup
- [ ] **Project Templates** - Cookiecutter for new tools
- [ ] **Shell Completion** - Bash/Zsh/Fish completions
- [ ] **Badge Collection** - CI status, coverage badges
- [ ] **ASCII Art Branding** - Professional terminal output
**Success Criteria:**
- [ ] Development environment setup < 10 minutes
- [ ] All commits pass automated quality checks
- [ ] CI/CD pipeline runs on every PR
- [ ] Documentation site auto-deploys
- [ ] Health check script validates setup
---
## Module 5.7: Phase 2 Enhancements (Infrastructure & Reliability)
**Goal**: Production-grade reliability, monitoring, and performance
**Note**: These enhance Phase 2 (Core Infrastructure). Add enterprise features for production deployment.
### Tasks (0/28)
#### High Priority
- [ ] **Debug Mode** - GAZEBO_MCP_DEBUG env var with verbose logging
- [ ] **Mock Mode** - Run without Gazebo for testing
- [ ] **Connection Pooling** - Reuse ROS2 connections
- [ ] **Health Check Endpoint** - HTTP /health for monitoring
- [ ] **Structured Logging** - JSON logs with request IDs
- [ ] **Graceful Degradation** - Continue on non-critical failures
- [ ] **Request/Response Logging** - Debug all MCP interactions
#### Medium Priority
- [ ] **Circuit Breaker Pattern** - Prevent cascade failures
- [ ] **Retry with Exponential Backoff** - Auto-retry failed operations
- [ ] **Message Batching** - Batch similar requests for efficiency
- [ ] **Configuration Hot-Reload** - Update config without restart
- [ ] **Prometheus Metrics** - Export server metrics
- [ ] **Connection State Machine** - Formal state management
- [ ] **Rate Limiting** - Protect server from overload
- [ ] **Request Caching** - Cache expensive operations
- [ ] **Async Queue System** - Background task processing
- [ ] **Dead Letter Queue** - Handle failed messages
#### Low Priority
- [ ] **Multiple ROS2 Nodes** - Distribute load across nodes
- [ ] **Service Discovery** - Auto-discover Gazebo services
- [ ] **Load Balancing** - Multiple Gazebo instances
- [ ] **Distributed Tracing** - OpenTelemetry integration
- [ ] **Blue-Green Deployment** - Zero-downtime updates
- [ ] **Webhook Support** - Event notifications
- [ ] **GraphQL API** - Alternative to MCP
- [ ] **WebSocket Support** - Real-time updates
- [ ] **Custom Middleware** - Plugin architecture
**Success Criteria:**
- [ ] Server handles 100+ req/sec
- [ ] <1% error rate under normal load
- [ ] Graceful handling of Gazebo crashes
- [ ] Metrics exported to Prometheus
- [ ] Debug mode provides actionable insights
---
## Module 5.8: Phase 3 Enhancements (Advanced Control & Multi-Robot)
**Goal**: Advanced robotics features, multi-robot coordination, and sensor fusion
**Note**: These enhance Phase 3 (Gazebo Control). Add research and production robotics capabilities.
### Tasks (0/27)
#### High Priority
- [ ] **Multi-Robot Coordination** - Spawn and control robot fleets
- [ ] **Named Waypoint System** - Define and navigate to named locations
- [ ] **Visual Debugging Tools** - Visualize robot paths, sensor data
- [ ] **RViz2 Integration** - Launch and configure RViz visualization
- [ ] **Improved Error Messages** - Context-aware, actionable errors
- [ ] **Smart Default Configurations** - Auto-detect robot type settings
#### Medium Priority
- [ ] **Path Planning Integration** - A* pathfinding for navigation
- [ ] **Nav2 Integration** - Full Nav2 stack support
- [ ] **Formation Control** - Robots maintain relative positions
- [ ] **Collision Avoidance** - Dynamic obstacle avoidance
- [ ] **Sensor Fusion** - Combine multiple sensor streams
- [ ] **State Machine Framework** - Robot behavior programming
- [ ] **Trajectory Recording** - Record and replay robot paths
- [ ] **Object Tracking** - Track moving objects in simulation
- [ ] **Calibration Tools** - Camera and sensor calibration
- [ ] **Teleoperation Modes** - Keyboard, gamepad, touch control
#### Low Priority
- [ ] **Swarm Behavior Primitives** - Flocking, swarming algorithms
- [ ] **Collaborative Mapping (SLAM)** - Multi-robot SLAM
- [ ] **Task Allocation** - Distribute tasks across robots
- [ ] **Behavior Trees** - Complex robot behaviors
- [ ] **Reinforcement Learning Integration** - RL training environment
- [ ] **Human-Robot Interaction** - Gestures, voice commands
- [ ] **Safety Zones** - Geofencing for robots
- [ ] **Battery Simulation** - Realistic power management
- [ ] **Wear and Tear Simulation** - Component degradation
- [ ] **Multi-Modal Control** - Voice, gesture, GUI control
- [ ] **AR/VR Integration** - Virtual reality visualization
**Success Criteria:**
- [ ] Can spawn and control 10+ robots simultaneously
- [ ] Nav2 integration works out-of-box
- [ ] Visual debugging available in RViz2
- [ ] Path planning finds valid paths
- [ ] Formation control maintains robot spacing
---
## Module 5.9: Phase 6 Enhancements (Testing & Quality Assurance)
**Goal**: Advanced testing, benchmarking, and continuous quality improvement
**Note**: These enhance Phase 6 (Testing). Add enterprise QA practices and automated quality gates.
### Tasks (0/18)
#### High Priority
- [ ] **Visual Test Reports** - HTML reports with screenshots
- [ ] **Continuous Benchmarking** - Track performance over time
- [ ] **Test Data Generators** - Generate test scenarios
- [ ] **Parallel Test Execution** - Run tests concurrently
- [ ] **Coverage Enforcement** - Fail CI if coverage drops
#### Medium Priority
- [ ] **Snapshot Testing** - Regression detection for outputs
- [ ] **Property-Based Testing** - Hypothesis for edge cases
- [ ] **Contract Testing** - Verify API contracts
- [ ] **Load Testing Suite** - Stress test server
- [ ] **Visual Regression Testing** - Detect UI changes
- [ ] **Test Fixtures Library** - Reusable test setups
- [ ] **Mutation Testing** - Verify test quality
- [ ] **Flaky Test Detection** - Identify unreliable tests
#### Low Priority
- [ ] **Chaos Engineering** - Inject failures during tests
- [ ] **Performance Regression Detection** - Alert on slowdowns
- [ ] **Test Impact Analysis** - Run only affected tests
- [ ] **Cross-Browser Testing** - Test web components
- [ ] **Accessibility Testing** - WCAG compliance
**Success Criteria:**
- [ ] Test suite runs in <5 minutes
- [ ] >90% code coverage maintained
- [ ] Zero flaky tests
- [ ] Performance benchmarks tracked
- [ ] Visual test reports generated
---
## Module 5.10: Automated Testing Pipelines & AI Reasoning ⭐ NEW
**Goal**: Enable MCP server to autonomously create, execute, and reason about complete testing pipelines in Gazebo
**Key Innovation**: AI-driven test outcome interpretation and root cause analysis - the system doesn't just collect data, it **understands** what the results mean.
**Note**: This is a game-changing capability - Claude can create entire test suites, run them, analyze failures, and suggest fixes, all through natural language.
### Tasks (0/25)
#### 🔴 HIGH PRIORITY - Test Pipeline Generation (7 tasks)
**Task 5.10.1: Automated Test Scenario Generator** ⏳
```python
async def generate_test_scenarios(
test_type: str, # "navigation", "manipulation", "perception", "integration"
coverage_target: str = "basic", # basic, comprehensive, edge_cases
difficulty_range: Tuple[str, str] = ("easy", "hard"),
num_scenarios: Optional[int] = None, # Auto-determine if None
seed: Optional[int] = None, # Reproducible generation
constraints: Optional[Dict] = None # Custom constraints
) -> OperationResult:
"""
Generate complete test scenarios automatically.
Example:
scenarios = await generate_test_scenarios(
test_type="navigation",
coverage_target="comprehensive",
difficulty_range=("medium", "expert"),
num_scenarios=50,
seed=42
)
# Returns structured test scenarios:
# - World configurations
# - Robot placements
# - Task definitions
# - Success criteria
# - Expected metrics
"""
```
**Use Cases**:
- Generate regression test suite from requirements
- Create benchmark scenarios for algorithm comparison
- Build edge case tests automatically
- Generate stress tests for multi-robot systems
---
**Task 5.10.2: Test Parameter Optimizer** ⏳
```python
async def optimize_test_parameters(
base_test: Dict,
optimization_goal: str, # "coverage", "difficulty", "realism", "speed"
iterations: int = 100,
evaluation_criteria: Optional[List[str]] = None
) -> OperationResult:
"""
Optimize test parameters using AI-driven search.
Example:
optimized = await optimize_test_parameters(
base_test={
"robot": "turtlebot3",
"obstacles": "random",
"goal_distance": 10.0
},
optimization_goal="difficulty",
iterations=50
)
# AI explores parameter space to maximize test difficulty
# while maintaining solvability
"""
```
**Capabilities**:
- Find optimal obstacle placements
- Tune task difficulty to target level
- Maximize test coverage
- Balance realism vs. execution speed
---
**Task 5.10.3: Test Suite Composer** ⏳
```python
async def compose_test_suite(
requirements: List[str], # Natural language requirements
test_types: Optional[List[str]] = None,
priority_order: str = "critical_first", # Order of test execution
parallel_groups: bool = True, # Group tests that can run in parallel
estimated_duration: Optional[float] = None # Target total duration (minutes)
) -> OperationResult:
"""
Compose complete test suite from requirements.
Example:
suite = await compose_test_suite(
requirements=[
"Robot must navigate through doorways",
"Robot must handle dynamic obstacles",
"Robot must recover from localization failures",
"Multi-robot coordination must be collision-free"
],
estimated_duration=15.0 # 15-minute test suite
)
# Returns:
# - Test execution plan
# - Parallel execution groups
# - Expected duration
# - Coverage map
"""
```
---
**Task 5.10.4: Benchmark World Generator** ⏳
```python
async def generate_benchmark_world(
benchmark_type: str, # "nav2_standard", "manipulation", "custom"
difficulty: str = "medium",
seed: Optional[int] = None,
export_ground_truth: bool = True, # Export perfect navigation paths
metadata: bool = True # Include world metadata for reproducibility
) -> OperationResult:
"""
Generate standardized benchmark worlds.
Example:
world = await generate_benchmark_world(
benchmark_type="nav2_standard",
difficulty="hard",
seed=42,
export_ground_truth=True
)
# Creates:
# - Reproducible world file
# - Ground truth optimal paths
# - Performance targets
# - Metadata for publication
"""
```
---
**Task 5.10.5: Test Matrix Builder** ⏳
```python
async def build_test_matrix(
variables: Dict[str, List], # Parameter variations
combinations: str = "full", # full, pairwise, custom
max_tests: Optional[int] = None # Limit combinatorial explosion
) -> OperationResult:
"""
Build comprehensive test matrices.
Example:
matrix = await build_test_matrix(
variables={
"robot_type": ["turtlebot3", "husky", "jackal"],
"obstacle_density": [0.1, 0.3, 0.5, 0.7],
"lighting": ["day", "night", "variable"],
"surface": ["flat", "rough", "mixed"]
},
combinations="pairwise", # Reduce from 144 to ~20 tests
max_tests=30
)
# Uses combinatorial testing to maximize coverage
# with minimum test count
"""
```
---
**Task 5.10.6: Regression Test Auto-Generator** ⏳
```python
async def generate_regression_tests(
failed_test: Dict, # Previously failed test
variation_count: int = 5, # Number of variations to generate
focus: str = "failure_boundary" # Where to focus variations
) -> OperationResult:
"""
Automatically generate regression tests around failures.
Example:
# After a test fails
regression_suite = await generate_regression_tests(
failed_test=test_results["navigation_test_42"],
variation_count=10,
focus="failure_boundary"
)
# Generates tests that explore:
# - Slightly easier versions (should pass)
# - Slightly harder versions (might fail)
# - Different failure modes
# - Edge cases near the failure
"""
```
---
**Task 5.10.7: Test Dependency Resolver** ⏳
```python
async def resolve_test_dependencies(
tests: List[Dict],
optimize_order: bool = True,
parallel_execution: bool = True
) -> OperationResult:
"""
Analyze and resolve test dependencies for optimal execution.
Example:
execution_plan = await resolve_test_dependencies(
tests=test_suite,
optimize_order=True,
parallel_execution=True
)
# Returns:
# - Dependency graph
# - Optimal execution order
# - Parallel execution groups
# - Estimated duration per group
"""
```
---
#### 🔴 HIGH PRIORITY - AI-Driven Outcome Reasoning (9 tasks) ⭐ CORE INNOVATION
**Task 5.10.8: Test Result Interpreter** ⏳
```python
async def interpret_test_results(
test_results: Dict,
context: Optional[Dict] = None, # Additional context for interpretation
detail_level: str = "summary", # summary, detailed, expert
include_recommendations: bool = True
) -> OperationResult:
"""
AI interprets test results in natural language.
Example:
interpretation = await interpret_test_results(
test_results=results,
detail_level="detailed",
include_recommendations=True
)
# Returns natural language summary:
# "Navigation Test Suite Results:
#
# Overall: 8/10 tests passed (80% success rate)
#
# Failures Analysis:
# - Test 'narrow_corridor': Robot got stuck at 4.2m into corridor
# Likely cause: Local planner failed to find valid trajectory
# Recommendation: Increase planner_patience parameter
#
# - Test 'dynamic_obstacles': Collision at t=23.5s
# Likely cause: Obstacle moved faster than prediction window
# Recommendation: Reduce max_obstacle_velocity or increase
# prediction_horizon
#
# Performance Trends:
# - Navigation time increased 15% vs. baseline
# - CPU usage within normal range
# - Success rate down from 90% (investigate cause)
#
# Next Steps:
# 1. Fix narrow_corridor issue (high priority)
# 2. Tune dynamic obstacle parameters
# 3. Run regression suite to verify improvements"
"""
```
**Key Feature**: This is NOT just data reporting - the AI **reasons** about what the data means, identifies root causes, and suggests fixes.
---
**Task 5.10.9: Failure Root Cause Analyzer** ⏳
```python
async def analyze_failure_root_cause(
failed_test: Dict,
sensor_logs: Optional[Dict] = None,
system_logs: Optional[List[str]] = None,
similar_failures: Optional[List[Dict]] = None # Historical data
) -> OperationResult:
"""
Deep analysis of test failures using AI reasoning.
Example:
root_cause = await analyze_failure_root_cause(
failed_test=navigation_failure,
sensor_logs=lidar_and_odometry_data,
system_logs=ros_logs,
similar_failures=past_navigation_failures
)
# AI analyzes:
# - When exactly did failure occur
# - What was robot state at failure moment
# - What changed leading up to failure
# - Pattern matching with historical failures
# - Environmental factors
# - System resource issues
#
# Returns structured root cause:
# {
# "primary_cause": "Localization divergence",
# "contributing_factors": [
# "Low feature environment (smooth walls)",
# "High odometry drift on slippery surface"
# ],
# "failure_timeline": [...],
# "evidence": {
# "covariance_explosion": "t=15.3s",
# "particle_filter_resampling_failures": 8
# },
# "confidence": 0.87,
# "recommendations": [...]
# }
"""
```
**Capabilities**:
- Correlate sensor data with failure timing
- Identify cascading failures
- Detect resource exhaustion
- Compare with known failure modes
- Suggest targeted fixes
---
**Task 5.10.10: Performance Anomaly Detector** ⏳
```python
async def detect_performance_anomalies(
current_results: Dict,
baseline: Optional[Dict] = None, # Historical baseline
sensitivity: str = "medium", # low, medium, high
metrics: Optional[List[str]] = None # Which metrics to check
) -> OperationResult:
"""
AI-powered anomaly detection in test performance.
Example:
anomalies = await detect_performance_anomalies(
current_results=latest_test_run,
baseline=last_30_runs_average,
sensitivity="high"
)
# Detects:
# "⚠️ Anomalies Detected:
#
# 1. Navigation time: 45.3s (expected: 32.1s ±5s)
# Severity: HIGH
# Confidence: 0.93
# Possible causes:
# - CPU throttling detected
# - Path planner took 3x longer than usual
# - May indicate regression in planning algorithm
#
# 2. Memory usage: 2.1GB (expected: 1.4GB ±0.2GB)
# Severity: MEDIUM
# Confidence: 0.78
# Possible causes:
# - Memory leak in sensor processing
# - Larger map than usual (104MB vs 67MB avg)
#
# 3. Success rate: 75% (expected: 90% ±3%)
# Severity: CRITICAL
# Confidence: 0.99
# Trend: Declining over last 5 runs
# Action required: Investigate immediately"
"""
```
---
**Task 5.10.11: Comparative Test Analyzer** ⏳
```python
async def compare_test_results(
test_groups: Dict[str, Dict], # Multiple test runs to compare
comparison_type: str = "algorithm", # algorithm, version, configuration
metrics: Optional[List[str]] = None,
statistical_tests: bool = True # Run statistical significance tests
) -> OperationResult:
"""
Compare multiple test results with AI interpretation.
Example:
comparison = await compare_test_results(
test_groups={
"baseline_algo": baseline_results,
"new_algo_v1": new_results_v1,
"new_algo_v2": new_results_v2
},
comparison_type="algorithm",
statistical_tests=True
)
# AI provides:
# "Algorithm Comparison Results:
#
# Performance Summary:
# - new_algo_v2: BEST overall (Winner)
# - 23% faster navigation (p<0.01, significant)
# - 12% higher success rate (p=0.04, significant)
# - 5% more CPU usage (p=0.34, not significant)
#
# - new_algo_v1: Mixed results
# - 8% faster than baseline (p=0.12, not significant)
# - Same success rate as baseline
# - 15% less CPU usage (p<0.01, significant)
#
# - baseline_algo: Slowest but stable
#
# Trade-off Analysis:
# If performance is critical: Choose new_algo_v2
# If CPU/power limited: Choose new_algo_v1
# If stability paramount: Keep baseline_algo
#
# Recommendation: Deploy new_algo_v2 to production.
# Confidence: HIGH (based on 50 test runs each)"
"""
```
---
**Task 5.10.12: Test Coverage Analyzer** ⏳
```python
async def analyze_test_coverage(
test_suite: Dict,
requirements: List[str],
code_coverage: Optional[Dict] = None,
scenario_coverage: bool = True
) -> OperationResult:
"""
AI analyzes what is and isn't being tested.
Example:
coverage = await analyze_test_coverage(
test_suite=current_suite,
requirements=system_requirements,
scenario_coverage=True
)
# AI identifies gaps:
# "Coverage Analysis:
#
# Requirement Coverage: 78% (14/18 requirements tested)
#
# ✅ Well-Covered Areas:
# - Basic navigation (8 tests, multiple scenarios)
# - Obstacle avoidance (12 tests, edge cases included)
# - Multi-robot coordination (6 tests)
#
# ⚠️ Partially Covered:
# - Localization recovery (only tested in 2 scenarios)
# Missing: High-speed recovery, outdoor scenarios
# - Battery management (tested but no edge cases)
#
# ❌ Not Covered:
# - Emergency stop behavior
# - Network failure recovery
# - Sensor failure modes
# - Extreme weather conditions
#
# Recommended Additional Tests:
# 1. Add 3 emergency stop scenarios (CRITICAL)
# 2. Add network partition tests (HIGH)
# 3. Add sensor degradation tests (MEDIUM)"
"""
```
---
**Task 5.10.13: Trend Analysis & Prediction** ⏳
```python
async def analyze_test_trends(
historical_results: List[Dict],
forecast_periods: int = 5,
metrics: Optional[List[str]] = None,
detect_regressions: bool = True
) -> OperationResult:
"""
Analyze trends in test results over time and predict future performance.
Example:
trends = await analyze_test_trends(
historical_results=last_100_test_runs,
forecast_periods=10,
detect_regressions=True
)
# AI detects patterns:
# "Trend Analysis (Last 100 Test Runs):
#
# 📈 Improving Metrics:
# - Success rate: Up from 85% to 92% over 50 runs
# Trend: Steady improvement (+0.14%/run)
# Forecast: Will reach 95% in ~20 more runs
#
# 📉 Declining Metrics: ⚠️
# - Average CPU usage: Up from 45% to 68%
# Trend: Accelerating increase
# Forecast: Will hit 100% in ~30 runs (CRITICAL)
# Likely cause: Memory leak or complexity creep
# Action: Investigate immediately
#
# ➡️ Stable Metrics:
# - Navigation time: 32s ±2s (stable)
# - Memory usage: 1.4GB ±0.1GB (stable)
#
# 🔴 Detected Regressions:
# - Run #87: Sudden 15% drop in success rate
# Recovered by run #91
# Correlates with: Code commit abc123
# Note: Flag for review"
"""
```
---
**Task 5.10.14: Success Criteria Validator** ⏳
```python
async def validate_success_criteria(
test_results: Dict,
success_criteria: Dict,
strict_mode: bool = False, # Fail on any violation
report_near_misses: bool = True # Warn on almost-failures
) -> OperationResult:
"""
Validate test results against defined success criteria with AI insights.
Example:
validation = await validate_success_criteria(
test_results=results,
success_criteria={
"success_rate": {"min": 90, "target": 95},
"avg_time": {"max": 35, "target": 30},
"cpu_usage": {"max": 70},
"zero_collisions": True
},
report_near_misses=True
)
# AI validates and explains:
# "Success Criteria Validation:
#
# ✅ PASSED: success_rate = 92%
# (Above minimum 90%, below target 95%)
#
# ✅ PASSED: avg_time = 32.1s
# (Below maximum 35s, slightly above target 30s)
# Note: Could optimize further for target
#
# ❌ FAILED: cpu_usage = 73%
# (Above maximum 70%)
# Severity: Violation by 4.3%
# Impact: May cause throttling on target hardware
#
# ⚠️ NEAR-MISS: zero_collisions = True
# (Technically passed but...)
# Warning: 2 near-collisions detected (clearance <10cm)
# Risk: Small parameter changes could cause failures
#
# Overall: CONDITIONAL PASS
# Action Required: Fix CPU usage before production deploy"
"""
```
---
**Task 5.10.15: Failure Pattern Recognition** ⏳
```python
async def recognize_failure_patterns(
test_results: List[Dict],
historical_failures: Optional[List[Dict]] = None,
pattern_types: Optional[List[str]] = None # Types to look for
) -> OperationResult:
"""
Recognize patterns in test failures using ML and AI reasoning.
Example:
patterns = await recognize_failure_patterns(
test_results=recent_failures,
historical_failures=all_past_failures
)
# AI identifies patterns:
# "Failure Pattern Analysis:
#
# Pattern #1: 'Narrow Corridor Syndrome' (confidence: 0.91)
# - Occurs: 12/15 times in corridors <1.5m wide
# - Timing: Typically at 60-70% through corridor
# - Root cause: Local planner oscillation
# - Affected tests: Tests 4, 7, 12, 18, 23
# - Fix: Increase DWA parameter min_vel_theta to 0.3
#
# Pattern #2: 'Friday Afternoon Effect' (confidence: 0.73)
# - Occurs: More failures on Friday afternoon runs
# - Correlation: Shared CI/CD infrastructure load
# - Impact: 15% higher failure rate
# - Not a robot issue: Infrastructure related
# - Fix: Run tests on dedicated hardware
#
# Pattern #3: 'Cold Start Failures' (confidence: 0.88)
# - Occurs: First test of the day fails more often
# - Root cause: AMCL not fully initialized
# - Fix: Add 5-second initialization delay
#
# Actionable Insights:
# - 67% of failures match known patterns
# - Fixing Pattern #1 would eliminate ~30% of failures
# - Patterns suggest fixes rather than algorithm issues"
"""
```
---
**Task 5.10.16: Test Result Summarizer** ⏳
```python
async def summarize_test_results(
test_results: Dict,
audience: str = "developers", # developers, managers, researchers
format: str = "natural_language", # natural_language, structured, email
include_visualizations: bool = True
) -> OperationResult:
"""
Generate audience-appropriate summaries of test results.
Example (for managers):
summary = await summarize_test_results(
test_results=suite_results,
audience="managers",
format="email"
)
# Generates executive summary:
# "Test Suite Summary - Navigation System v2.3
#
# Overall Status: ✅ PASS WITH RECOMMENDATIONS
#
# Key Metrics:
# • 92% success rate (target: 90%) ✅
# • 32.1s average completion time ✅
# • 73% CPU usage (target: <70%) ⚠️
#
# Highlights:
# ✅ 8% improvement in success rate vs. v2.2
# ✅ Algorithm handles complex scenarios well
# ⚠️ Performance issue needs attention before deploy
#
# Recommendation: APPROVE with CPU optimization
# Estimated fix time: 2-3 days
#
# Details: See attached full report"
#
# Example (for developers):
# "Navigation System v2.3 - Detailed Test Results
#
# Suite: nav2_comprehensive (50 tests, 45min runtime)
#
# Results Breakdown:
# ✅ 46 passed (92%)
# ❌ 4 failed (8%)
#
# Failed Tests:
# 1. narrow_corridor_expert [Line 245]
# Error: Local planner timeout
# Repro: 100% (seed=42)
# Fix: Try PR #234 (increases planner_patience)
# ..."
"""
```
---
#### 🟡 MEDIUM PRIORITY - Test Execution & Automation (5 tasks)
**Task 5.10.17: Parallel Test Executor** ⏳
```python
async def execute_tests_parallel(
test_suite: Dict,
max_parallel: int = 4,
resource_management: bool = True,
fail_fast: bool = False # Stop on first failure
) -> OperationResult:
"""
Execute tests in parallel with resource management.
"""
```
**Task 5.10.18: Continuous Test Monitor** ⏳
```python
async def monitor_test_execution(
test_run_id: str,
realtime_updates: bool = True,
intervention_enabled: bool = False # Allow human intervention
) -> OperationResult:
"""
Monitor test execution with real-time status and intervention capability.
"""
```
**Task 5.10.19: Test Data Collector** ⏳
```python
async def collect_test_data(
test_execution: Dict,
data_types: List[str], # sensor, performance, logs, video
compression: bool = True,
storage_location: Optional[str] = None
) -> OperationResult:
"""
Comprehensive test data collection during execution.
"""
```
**Task 5.10.20: Adaptive Test Scheduler** ⏳
```python
async def schedule_tests_adaptive(
test_suite: Dict,
available_time: float, # minutes
priority_mode: str = "risk_based", # risk, coverage, random
learn_from_history: bool = True
) -> OperationResult:
"""
Intelligently schedule tests based on risk and available time.
If you only have 10 minutes, run the tests most likely to catch issues.
"""
```
**Task 5.10.21: Test Replay & Debug** ⏳
```python
async def replay_failed_test(
failed_test: Dict,
debug_mode: bool = True,
slow_motion: Optional[float] = None, # Slow down by factor
breakpoints: Optional[List[float]] = None # Pause at these timestamps
) -> OperationResult:
"""
Replay failed tests with debugging capabilities.
"""
```
---
#### 🟢 LOW PRIORITY - Advanced Features (4 tasks)
**Task 5.10.22: Metamorphic Test Generator** ⏳
```python
async def generate_metamorphic_tests(
base_test: Dict,
transformations: List[str], # "mirror", "scale", "rotate", "reverse"
verify_properties: List[str] # Properties that should hold
) -> OperationResult:
"""
Generate metamorphic tests - transformations that should preserve properties.
Example: If robot navigates from A to B successfully, it should also
navigate from B to A successfully (reverse transformation).
"""
```
**Task 5.10.23: Fuzz Test Generator** ⏳
```python
async def generate_fuzz_tests(
api_specification: Dict,
chaos_level: str = "medium", # low, medium, high, extreme
target_coverage: float = 0.9
) -> OperationResult:
"""
Generate fuzz tests for API and edge cases.
"""
```
**Task 5.10.24: Test Oracle Generator** ⏳
```python
async def generate_test_oracle(
test_scenario: Dict,
oracle_type: str = "physics_based", # physics, statistical, learned
confidence_threshold: float = 0.95
) -> OperationResult:
"""
Generate test oracles - expected outcomes for tests.
Uses physics simulation or learned models to predict what should happen.
"""
```
**Task 5.10.25: Test Evolution System** ⏳
```python
async def evolve_test_suite(
current_suite: Dict,
mutation_rate: float = 0.1,
selection_criteria: str = "defect_detection",
generations: int = 10
) -> OperationResult:
"""
Evolve test suite using genetic algorithms.
Creates variations of tests, runs them, keeps the ones that find bugs.
"""
```
---
### Real-World Usage Example
```python
# SCENARIO: Developer wants to validate a new navigation algorithm
# 1. Generate comprehensive test scenarios
test_scenarios = await generate_test_scenarios(
test_type="navigation",
coverage_target="comprehensive",
difficulty_range=("easy", "expert"),
num_scenarios=30,
seed=42 # Reproducible
)
# 2. Run tests in parallel
results = await execute_tests_parallel(
test_suite=test_scenarios.data["scenarios"],
max_parallel=4,
resource_management=True
)
# 3. AI analyzes results and explains what happened
interpretation = await interpret_test_results(
test_results=results.data,
detail_level="detailed",
include_recommendations=True
)
print(interpretation.data["summary"])
# Output:
# "Navigation Algorithm Test Results:
#
# Overall: 27/30 tests passed (90% success rate)
#
# ✅ Strengths:
# - Excellent performance on simple and medium difficulty (100% pass)
# - 15% faster than baseline algorithm
# - Good CPU efficiency
#
# ❌ Issues Found:
# - 3 failures on expert-level narrow corridor scenarios
# - Root cause: Local planner oscillation in tight spaces
# - Affects ~10% of real-world scenarios
#
# 📊 Performance vs. Baseline:
# - Speed: +15% faster ✅
# - Success: -5% lower ⚠️
# - CPU: -10% more efficient ✅
#
# 💡 Recommendations:
# 1. Tune local planner parameters for narrow spaces
# 2. Add corridor-specific behavior mode
# 3. Re-run expert tests after tuning
#
# Confidence: This algorithm is production-ready for most scenarios,
# but needs refinement for narrow indoor environments."
# 4. Deep dive on failures
for failure in interpretation.data["failures"]:
root_cause = await analyze_failure_root_cause(
failed_test=failure,
sensor_logs=results.data["sensor_logs"][failure["id"]],
system_logs=results.data["system_logs"][failure["id"]]
)
print(f"Failure: {failure['name']}")
print(f"Root cause: {root_cause.data['primary_cause']}")
print(f"Fix: {root_cause.data['recommendations'][0]}")
# 5. Generate regression tests around failures
regression_suite = await generate_regression_tests(
failed_test=interpretation.data["failures"][0],
variation_count=10,
focus="failure_boundary"
)
# 6. Compare with baseline algorithm
comparison = await compare_test_results(
test_groups={
"baseline": baseline_results,
"new_algorithm": results.data
},
comparison_type="algorithm",
statistical_tests=True
)
print(comparison.data["recommendation"])
# "Recommendation: Deploy new_algorithm to staging for further validation.
# It shows significant performance improvements but needs narrow corridor
# refinement before production deployment."
```
---
### Integration with Existing Phases
**Works with Phase 4 (World Generation)**:
- Uses world generation tools to create test environments
- Generates obstacle courses for navigation tests
- Creates benchmark worlds
**Works with Phase 6 (Testing)**:
- Enhances basic testing with AI reasoning
- Automates test generation and execution
- Provides intelligent test analysis
**Works with Phase 2 (Infrastructure)**:
- Integrates with CI/CD pipelines
- Uses metrics and logging systems
- Leverages health monitoring
---
### Success Criteria
- [ ] Can generate 30+ test scenarios from natural language description
- [ ] Can execute test suite and collect comprehensive data
- [ ] AI provides accurate root cause analysis (>85% accuracy)
- [ ] Test result summaries are clear and actionable
- [ ] Reduces manual test analysis time by >70%
- [ ] Detects regressions within 1 test run
- [ ] Provides statistically sound algorithm comparisons
- [ ] Identifies failure patterns across test runs
---
### Implementation Priority
**Week 1-2 (Core Value)**:
1. Test Scenario Generator (5.10.1)
2. Test Result Interpreter (5.10.8)
3. Failure Root Cause Analyzer (5.10.9)
4. Parallel Test Executor (5.10.17)
**Week 3-4 (Enhanced Analysis)**:
5. Performance Anomaly Detector (5.10.10)
6. Comparative Test Analyzer (5.10.11)
7. Test Suite Composer (5.10.3)
8. Test Data Collector (5.10.19)
**Week 5+ (Advanced Features)**:
- Remaining tasks as needed
---
**This module transforms testing from "run and check" to "understand and improve" - the AI becomes your test analysis expert.** 🧠✨
---
## Module 5.11: Advanced MCP Protocol Features ⭐ NEW
**Goal**: Leverage advanced MCP (Model Context Protocol) capabilities to create a more intelligent, self-documenting, and autonomous MCP server
**Key Innovation**: These features transform the MCP server from a "tool provider" to an "intelligent agent" that can guide users, access rich resources, and perform complex reasoning autonomously.
**Note**: These are official MCP protocol features that unlock powerful new interaction patterns.
### Tasks (0/30)
---
## Part 1: MCP Prompts (8 tasks)
**What are Prompts?** MCP prompts are pre-defined, reusable prompt templates that the server exposes to clients. They guide users through common tasks with optimal phrasing.
**Why powerful?** Users don't need to know exact commands - they can select from curated prompts that demonstrate best practices.
---
#### 🔴 HIGH PRIORITY - Core Prompts (5 tasks)
**Task 5.11.1: Robot Setup Prompts** ⏳
```python
# Expose prompts for common robot setup scenarios
PROMPTS = {
"quick-start-turtlebot3": {
"name": "Quick Start: TurtleBot3",
"description": "Spawn a TurtleBot3 and test basic navigation",
"arguments": [
{
"name": "world_type",
"description": "Type of world to use",
"required": False,
"default": "empty"
},
{
"name": "difficulty",
"description": "Navigation difficulty",
"required": False,
"default": "easy"
}
],
"prompt_template": """Please help me set up a TurtleBot3 robot for testing:
1. Create a {{world_type}} world with {{difficulty}} difficulty obstacles
2. Spawn a TurtleBot3 Waffle robot at the center
3. Verify all sensors are working (LiDAR, camera, IMU)
4. Test basic movement: drive forward 2 meters
5. Run a simple navigation test to a goal 5 meters away
Please provide real-time feedback on each step."""
},
"debug-navigation-failure": {
"name": "Debug Navigation Failure",
"description": "Diagnose why robot navigation failed",
"arguments": [
{
"name": "test_name",
"description": "Name of the failed test",
"required": True
}
],
"prompt_template": """My robot navigation test "{{test_name}}" just failed.
Please help me debug:
1. Check the robot's last known position
2. Review sensor data from the time of failure
3. Analyze the navigation logs for errors
4. Check for localization issues (AMCL covariance)
5. Identify the root cause
6. Suggest specific fixes with parameters to tune
Be thorough and explain your reasoning."""
},
"benchmark-algorithm": {
"name": "Benchmark Navigation Algorithm",
"description": "Compare navigation algorithms rigorously",
"arguments": [
{
"name": "algorithm_name",
"description": "Name of algorithm to benchmark",
"required": True
},
{
"name": "num_trials",
"description": "Number of test trials",
"required": False,
"default": "30"
}
],
"prompt_template": """I want to benchmark my navigation algorithm "{{algorithm_name}}"
against the baseline.
Please:
1. Generate {{num_trials}} diverse test scenarios (easy to expert)
2. Run tests with both baseline and {{algorithm_name}}
3. Collect metrics: success rate, time, CPU, path efficiency
4. Perform statistical significance tests
5. Identify scenarios where each algorithm excels
6. Provide a deployment recommendation
Use reproducible seeds for fair comparison."""
},
"create-tutorial-world": {
"name": "Create Tutorial World",
"description": "Generate an educational simulation world",
"arguments": [
{
"name": "topic",
"description": "What to teach",
"required": True
},
{
"name": "difficulty",
"description": "Student level",
"required": False,
"default": "beginner"
}
],
"prompt_template": """Create a tutorial world for teaching {{topic}} to {{difficulty}}-level students.
Requirements:
1. Design progressive challenges (3-5 difficulty levels)
2. Include clear visual markers and objectives
3. Add safety boundaries
4. Create instructor notes explaining learning objectives
5. Generate student handout with tasks
6. Include success criteria for each challenge
Make it engaging and educational!"""
},
"optimize-performance": {
"name": "Optimize System Performance",
"description": "Find and fix performance bottlenecks",
"prompt_template": """My simulation is running slower than expected.
Please help optimize:
1. Profile current performance (CPU, memory, FPS)
2. Identify bottlenecks (rendering, physics, ROS2 messages)
3. Suggest specific optimizations:
- Reduce sensor update rates if safe
- Optimize world complexity
- Tune physics parameters
- Configure ROS2 QoS settings
4. Implement top 3 optimizations
5. Measure improvement
Target: 30+ FPS, <70% CPU usage"""
}
}
```
**Use Cases**:
- New users discover capabilities through prompts
- Best practices encoded in prompt templates
- Consistent task execution across team
- Self-documenting server capabilities
---
**Task 5.11.2: Multi-Robot Coordination Prompts** ⏳
```python
PROMPTS = {
"swarm-demo": {
"name": "Swarm Robotics Demo",
"description": "Create impressive multi-robot coordination demo",
"arguments": [
{
"name": "num_robots",
"description": "Fleet size",
"required": False,
"default": "5"
},
{
"name": "behavior",
"description": "Swarm behavior pattern",
"required": False,
"default": "formation"
}
],
"prompt_template": """Create a swarm robotics demonstration with {{num_robots}} robots
showing {{behavior}} behavior.
Demo flow:
1. Spawn {{num_robots}} robots in starting formation
2. Demonstrate swarm {{behavior}}:
- If formation: V-shape, line, circle patterns
- If flocking: Boid-like emergent behavior
- If coverage: Systematic area coverage
3. Add dynamic obstacles to show collision avoidance
4. Show task allocation (assign targets to robots)
5. Demonstrate fault tolerance (remove one robot mid-demo)
Make it visually impressive!"""
}
}
```
---
**Task 5.11.3: Research Experiment Prompts** ⏳
```python
PROMPTS = {
"reproducible-experiment": {
"name": "Reproducible Research Experiment",
"description": "Set up experiment with full reproducibility",
"arguments": [
{
"name": "experiment_description",
"description": "What you're testing",
"required": True
}
],
"prompt_template": """Set up a reproducible experiment for: {{experiment_description}}
Requirements for publication-ready experiment:
1. Create experiment with fixed seed for reproducibility
2. Generate exactly 50 trials with controlled variation
3. Record ALL parameters (world config, robot config, algorithm params)
4. Collect comprehensive metrics (success, time, path length, CPU, etc.)
5. Export data in standard format (CSV, JSON)
6. Generate plots (success rate, performance curves)
7. Perform statistical analysis (mean, std, confidence intervals)
8. Create metadata file for reproduction
This must be reproducible by other researchers!"""
}
}
```
---
**Task 5.11.4: Educational Prompts** ⏳
```python
PROMPTS = {
"explain-concept": {
"name": "Explain Robotics Concept",
"description": "Learn a robotics concept through interactive demo",
"arguments": [
{
"name": "concept",
"description": "What to learn",
"required": True
}
],
"prompt_template": """Teach me about {{concept}} using interactive simulation.
Please:
1. Explain the concept in simple terms
2. Create a visual demonstration in Gazebo
3. Show me the effect by varying parameters
4. Explain what I'm seeing and why it matters
5. Give me hands-on exercises to try
6. Suggest related concepts to explore
Make it interactive and intuitive!"""
}
}
```
---
**Task 5.11.5: Prompt Discovery & Management** ⏳
```python
async def list_available_prompts(
category: Optional[str] = None, # setup, debug, benchmark, education, etc.
search_query: Optional[str] = None
) -> OperationResult:
"""
List all available prompts with descriptions.
Users can browse and discover prompts:
- By category (setup, debug, etc.)
- By search query
- View detailed descriptions
- See example usage
"""
async def get_prompt_template(
prompt_name: str,
arguments: Dict[str, Any]
) -> OperationResult:
"""
Get a rendered prompt with user's arguments filled in.
Example:
prompt = await get_prompt_template(
prompt_name="quick-start-turtlebot3",
arguments={
"world_type": "warehouse",
"difficulty": "medium"
}
)
# Returns ready-to-use prompt:
# "Please help me set up a TurtleBot3 robot for testing:
# 1. Create a warehouse world with medium difficulty obstacles..."
"""
```
---
#### 🟡 MEDIUM PRIORITY - Specialized Prompts (3 tasks)
**Task 5.11.6: Troubleshooting Prompts** ⏳
- "Why is my robot not moving?"
- "Debug sensor issues"
- "Fix localization problems"
- "Resolve ROS2 communication errors"
**Task 5.11.7: Advanced Feature Prompts** ⏳
- "Set up dynamic reconfigure for live tuning"
- "Create custom behavior tree"
- "Implement recovery behaviors"
- "Set up multi-machine ROS2 network"
**Task 5.11.8: Integration Prompts** ⏳
- "Connect real hardware to simulation"
- "Set up hardware-in-the-loop testing"
- "Bridge with external planning framework"
- "Export to real robot deployment"
---
## Part 2: MCP Resources (8 tasks)
**What are Resources?** MCP resources are URIs that the server exposes, allowing clients to read structured data like documentation, configuration files, world definitions, etc.
**Why powerful?** Claude can directly access and understand your project's structure, documentation, and data without requiring file system access.
---
#### 🔴 HIGH PRIORITY - Core Resources (5 tasks)
**Task 5.11.9: Documentation Resources** ⏳
```python
RESOURCES = {
"docs://api/tools": {
"uri": "docs://api/tools",
"name": "MCP Tools API Reference",
"description": "Complete API documentation for all MCP tools",
"mimeType": "text/markdown",
"
": lambda: read_markdown("docs/API_REFERENCE.md")
},
"docs://tutorials/getting-started": {
"uri": "docs://tutorials/getting-started",
"name": "Getting Started Tutorial",
"description": "Step-by-step guide for new users",
"mimeType": "text/markdown",
"content": lambda: read_markdown("docs/tutorials/getting_started.md")
},
"docs://examples/navigation": {
"uri": "docs://examples/navigation",
"name": "Navigation Examples",
"description": "Collection of navigation code examples",
"mimeType": "text/markdown",
"content": lambda: generate_examples_doc("navigation")
}
}
# Claude can now ask:
# "Read docs://api/tools to understand available tools"
# "Show me docs://tutorials/getting-started"
# "What examples are in docs://examples/navigation?"
```
**Use Cases**:
- Claude reads docs to answer questions accurately
- Users get context-aware help
- Self-documenting system
- Always up-to-date information
---
**Task 5.11.10: World & Model Resources** ⏳
```python
RESOURCES = {
"world://templates/warehouse": {
"uri": "world://templates/warehouse",
"name": "Warehouse World Template",
"description": "Configurable warehouse environment",
"mimeType": "application/sdf+xml",
"content": lambda: read_world_template("warehouse.sdf")
},
"models://robots/turtlebot3/specs": {
"uri": "models://robots/turtlebot3/specs",
"name": "TurtleBot3 Specifications",
"description": "Technical specs, dimensions, sensor configs",
"mimeType": "application/json",
"content": lambda: get_robot_specs("turtlebot3")
},
"models://objects/catalog": {
"uri": "models://objects/catalog",
"name": "Object Model Catalog",
"description": "All available objects for world building",
"mimeType": "application/json",
"content": lambda: list_available_models()
}
}
# Claude can inspect worlds and models:
# "What's in world://templates/warehouse?"
# "Show me models://robots/turtlebot3/specs"
# "List all models in models://objects/catalog"
```
---
**Task 5.11.11: Configuration Resources** ⏳
```python
RESOURCES = {
"config://server/current": {
"uri": "config://server/current",
"name": "Current Server Configuration",
"description": "Active MCP server configuration",
"mimeType": "application/json",
"content": lambda: get_current_config()
},
"config://ros2/qos-profiles": {
"uri": "config://ros2/qos-profiles",
"name": "ROS2 QoS Profiles",
"description": "Available Quality of Service configurations",
"mimeType": "application/json",
"content": lambda: get_qos_profiles()
},
"config://nav2/default-params": {
"uri": "config://nav2/default-params",
"name": "Nav2 Default Parameters",
"description": "Default navigation stack parameters",
"mimeType": "application/yaml",
"content": lambda: read_yaml("configs/nav2_default.yaml")
}
}
```
---
**Task 5.11.12: Runtime State Resources** ⏳
```python
RESOURCES = {
"state://gazebo/current": {
"uri": "state://gazebo/current",
"name": "Current Gazebo State",
"description": "Live simulation state",
"mimeType": "application/json",
"content": lambda: get_gazebo_state()
# Returns: running/paused, FPS, loaded models, etc.
},
"state://robots/active": {
"uri": "state://robots/active",
"name": "Active Robots",
"description": "All robots currently in simulation",
"mimeType": "application/json",
"content": lambda: list_active_robots()
# Returns: robot IDs, positions, states, sensor status
},
"state://performance/metrics": {
"uri": "state://performance/metrics",
"name": "Performance Metrics",
"description": "Real-time performance data",
"mimeType": "application/json",
"content": lambda: get_performance_metrics()
# Returns: CPU, memory, FPS, network, latency
}
}
# Dynamic resources update in real-time
# Claude can monitor: "Check state://performance/metrics"
```
---
**Task 5.11.13: Test & Benchmark Resources** ⏳
```python
RESOURCES = {
"tests://results/latest": {
"uri": "tests://results/latest",
"name": "Latest Test Results",
"description": "Most recent test execution results",
"mimeType": "application/json",
"content": lambda: get_latest_test_results()
},
"benchmarks://standard/nav2": {
"uri": "benchmarks://standard/nav2",
"name": "Nav2 Standard Benchmark",
"description": "Industry standard navigation benchmark",
"mimeType": "application/json",
"content": lambda: get_benchmark_definition("nav2_standard")
},
"tests://history/trends": {
"uri": "tests://history/trends",
"name": "Test Result Trends",
"description": "Historical test performance data",
"mimeType": "application/json",
"content": lambda: get_test_trends(days=30)
}
}
```
---
#### 🟡 MEDIUM PRIORITY - Extended Resources (3 tasks)
**Task 5.11.14: Research Data Resources** ⏳
- Experiment datasets
- Published results
- Ground truth data
- Comparison baselines
**Task 5.11.15: Learning Resources** ⏳
- Video tutorials
- Interactive examples
- Troubleshooting guides
- FAQ database
**Task 5.11.16: Community Resources** ⏳
- Shared worlds
- Community robot models
- User-contributed examples
- Plugin repository
---
## Part 3: MCP Sampling (7 tasks)
**What is Sampling?** MCP sampling allows the server to request LLM completions from the client. The server can ask Claude to reason about complex problems and use that reasoning in its logic.
**Why powerful?** The server becomes truly intelligent - it can use Claude's reasoning for complex decisions, not just execute pre-programmed logic.
---
#### 🔴 HIGH PRIORITY - Core Sampling (4 tasks)
**Task 5.11.17: Intelligent Path Planning with Sampling** ⏳
```python
async def plan_path_intelligent(
start: Pose,
goal: Pose,
obstacles: List[Obstacle],
robot_capabilities: Dict
) -> OperationResult:
"""
Use LLM sampling to reason about optimal path planning strategy.
Example:
# Server requests Claude to reason about path planning
reasoning = await sample_llm(
prompt=f'''
I need to plan a path for a robot with these capabilities:
{robot_capabilities}
From: {start}
To: {goal}
Obstacles: {obstacles}
Given the environment, what path planning strategy would work best?
Consider:
- Obstacle density and distribution
- Robot's turning radius and speed limits
- Goal urgency vs. safety tradeoffs
- Computational resources available
Recommend: algorithm choice, parameters to use, and explain why.
''',
max_tokens=500
)
# Use Claude's reasoning to configure planner
strategy = parse_strategy(reasoning)
return execute_plan_with_strategy(strategy)
```
**This is revolutionary**: The server doesn't just follow rules - it reasons about the situation and makes intelligent decisions.
---
**Task 5.11.18: Adaptive Test Generation with Sampling** ⏳
```python
async def generate_adaptive_tests(
system_under_test: str,
previous_failures: List[Dict],
coverage_gaps: List[str]
) -> OperationResult:
"""
Use LLM to design tests that target weaknesses.
Example:
test_design = await sample_llm(
prompt=f'''
I'm testing {system_under_test}.
Previous failures:
{json.dumps(previous_failures, indent=2)}
Coverage gaps:
{coverage_gaps}
Design 5 new tests that:
1. Target the failure patterns we've seen
2. Cover the gaps in our test suite
3. Are likely to find new bugs
4. Test edge cases we haven't considered
For each test, provide:
- Test scenario description
- Why this test is valuable
- Expected difficulty level
- Success criteria
Be creative and think adversarially!
''',
max_tokens=1000
)
return parse_and_generate_tests(test_design)
```
---
**Task 5.11.19: Failure Diagnosis with Sampling** ⏳
```python
async def diagnose_failure_intelligent(
test_name: str,
sensor_data: Dict,
system_logs: List[str],
robot_state_history: List[Dict]
) -> OperationResult:
"""
Deep reasoning about test failures.
Example:
diagnosis = await sample_llm(
prompt=f'''
Test "{test_name}" failed. Help me diagnose the root cause.
Sensor data at failure:
{format_sensor_data(sensor_data)}
System logs (last 50 lines):
{'\n'.join(system_logs[-50:])}
Robot state history (last 30 seconds):
{format_state_history(robot_state_history)}
Analyze:
1. When exactly did failure occur?
2. What was happening in the moments before?
3. Were there any warning signs in the data?
4. What's the most likely root cause?
5. What are alternative explanations?
6. How can we fix this?
7. How can we prevent similar failures?
Be thorough and consider all evidence.
''',
max_tokens=1500
)
return structure_diagnosis(diagnosis)
```
---
**Task 5.11.20: Configuration Optimization with Sampling** ⏳
```python
async def optimize_configuration_intelligent(
current_config: Dict,
performance_metrics: Dict,
target_goals: Dict
) -> OperationResult:
"""
Reason about configuration improvements.
Example:
recommendations = await sample_llm(
prompt=f'''
Current configuration:
{json.dumps(current_config, indent=2)}
Performance metrics:
- Success rate: {performance_metrics['success_rate']}%
- Average time: {performance_metrics['avg_time']}s
- CPU usage: {performance_metrics['cpu_usage']}%
Target goals:
- Success rate: >{target_goals['min_success']}%
- Time: <{target_goals['max_time']}s
- CPU: <{target_goals['max_cpu']}%
Analyze the configuration and suggest specific parameter changes
to meet the goals. For each suggestion:
1. Parameter name
2. Current value
3. Recommended value
4. Expected impact
5. Risk level (low/medium/high)
6. Why this change helps
Prioritize by impact and safety.
''',
max_tokens=1000
)
return parse_recommendations(recommendations)
```
---
#### 🟡 MEDIUM PRIORITY - Advanced Sampling (3 tasks)
**Task 5.11.21: Multi-Step Reasoning Tasks** ⏳
```python
async def solve_complex_task_with_reasoning(
task_description: str
) -> OperationResult:
"""
Break down complex tasks using LLM reasoning.
Example: "Create a complete autonomous delivery robot demo"
Server samples LLM to:
1. Break task into steps
2. Reason about dependencies
3. Identify potential issues
4. Generate execution plan
5. Execute plan with feedback loop
"""
```
**Task 5.11.22: Code Generation with Sampling** ⏳
```python
async def generate_custom_behavior(
behavior_description: str,
constraints: List[str]
) -> OperationResult:
"""
Generate custom robot behaviors on-the-fly.
Server samples LLM to write Python code for custom behaviors,
validates it, and executes safely.
"""
```
**Task 5.11.23: Interactive Tutoring with Sampling** ⏳
```python
async def provide_interactive_help(
user_question: str,
context: Dict
) -> OperationResult:
"""
Context-aware help system.
Server samples LLM with full context of current simulation state,
user's history, and question to provide intelligent assistance.
"""
```
---
## Part 4: Extended Thinking (7 tasks)
**What is Extended Thinking?** Extended thinking allows Claude to take more time reasoning about complex problems before responding. Perfect for robotics challenges that require deep analysis.
**Why powerful?** Complex robotics problems (path planning in cluttered environments, multi-objective optimization, failure diagnosis) benefit from extended reasoning time.
---
#### 🔴 HIGH PRIORITY - Core Extended Thinking (4 tasks)
**Task 5.11.24: Complex World Generation with Extended Thinking** ⏳
```python
async def generate_complex_world_thoughtful(
requirements: List[str],
constraints: Dict,
optimization_goals: List[str]
) -> OperationResult:
"""
Generate worlds that meet complex requirements.
Example:
world = await generate_complex_world_thoughtful(
requirements=[
"Must test narrow corridor navigation",
"Include dynamic obstacles",
"Test localization in low-feature areas",
"Provide multiple difficulty paths",
"Include emergency scenarios"
],
constraints={
"max_size": "20x20 meters",
"robot_type": "turtlebot3",
"must_be_solvable": True
},
optimization_goals=[
"Maximize test coverage",
"Balance difficulty progression",
"Minimize redundancy"
],
enable_extended_thinking=True # ← Give Claude more time to think
)
Extended thinking helps Claude:
- Consider trade-offs between requirements
- Reason about spatial layouts
- Validate solvability
- Optimize for multiple goals simultaneously
"""
```
---
**Task 5.11.25: Multi-Robot Task Allocation with Extended Thinking** ⏳
```python
async def allocate_tasks_optimal(
robots: List[Robot],
tasks: List[Task],
objectives: List[str], # minimize_time, balance_load, maximize_robustness, etc.
constraints: Dict
) -> OperationResult:
"""
Optimal task allocation for robot fleets.
Example:
allocation = await allocate_tasks_optimal(
robots=[robot1, robot2, robot3, robot4, robot5],
tasks=list_of_50_delivery_tasks,
objectives=[
"minimize_total_completion_time",
"balance_workload_across_robots",
"maximize_fault_tolerance",
"minimize_conflicts"
],
constraints={
"max_tasks_per_robot": 15,
"battery_limits": True,
"priority_tasks_first": True
},
enable_extended_thinking=True
)
Extended thinking allows reasoning about:
- Multi-objective optimization trade-offs
- Complex constraint satisfaction
- Failure scenarios and recovery
- Dynamic re-allocation strategies
"""
```
---
**Task 5.11.26: Algorithm Selection & Tuning with Extended Thinking** ⏳
```python
async def select_and_tune_algorithm(
problem_description: str,
environment_characteristics: Dict,
robot_capabilities: Dict,
performance_requirements: Dict
) -> OperationResult:
"""
Reason about which algorithm to use and how to configure it.
Example:
config = await select_and_tune_algorithm(
problem_description="Navigate through crowded warehouse",
environment_characteristics={
"obstacle_density": "high",
"obstacles_dynamic": True,
"space_constraints": "tight corridors",
"environment_changes": "frequent"
},
robot_capabilities={
"max_speed": 0.5,
"turning_radius": 0.2,
"sensors": ["lidar", "camera", "imu"],
"compute_power": "medium"
},
performance_requirements={
"success_rate": ">95%",
"max_time_per_task": "120s",
"safety": "critical",
"efficiency": "important"
},
enable_extended_thinking=True
)
Extended thinking helps choose between:
- DWA vs TEB vs MPC local planners
- A* vs RRT* vs informed RRT* global planners
- Parameter settings for chosen algorithms
- Fallback strategies
"""
```
---
**Task 5.11.27: Comprehensive System Diagnosis with Extended Thinking** ⏳
```python
async def diagnose_system_comprehensive(
symptoms: List[str],
system_logs: Dict,
performance_history: List[Dict],
recent_changes: List[str]
) -> OperationResult:
"""
Deep system-wide diagnosis.
Example:
diagnosis = await diagnose_system_comprehensive(
symptoms=[
"Navigation success rate dropped from 95% to 75%",
"CPU usage increased by 30%",
"Occasional localization divergence",
"Slower path planning"
],
system_logs={...}, # Last week of logs
performance_history=[...], # Last 100 test runs
recent_changes=[
"Updated Nav2 to v1.2.0",
"Added additional sensor",
"Changed QoS settings"
],
enable_extended_thinking=True
)
Extended thinking helps:
- Correlate multiple symptoms
- Consider cascading failures
- Evaluate recent changes impact
- Rule out false leads
- Identify root cause vs symptoms
"""
```
---
#### 🟡 MEDIUM PRIORITY - Advanced Extended Thinking (3 tasks)
**Task 5.11.28: Research Experiment Design** ⏳
```python
async def design_research_experiment(
research_question: str,
hypotheses: List[str],
available_resources: Dict
) -> OperationResult:
"""
Design rigorous scientific experiments.
Extended thinking helps:
- Design proper controls
- Select appropriate sample sizes
- Identify confounding variables
- Plan statistical analysis
- Consider experimental limitations
"""
```
**Task 5.11.29: Safety Analysis & Verification** ⏳
```python
async def analyze_safety_comprehensive(
system_description: Dict,
operating_conditions: Dict,
safety_requirements: List[str]
) -> OperationResult:
"""
Thorough safety analysis.
Extended thinking helps identify:
- Edge cases and failure modes
- Safety-critical scenarios
- Mitigation strategies
- Verification methods
"""
```
**Task 5.11.30: Architecture Design Recommendations** ⏳
```python
async def recommend_system_architecture(
project_requirements: Dict,
constraints: Dict,
future_needs: List[str]
) -> OperationResult:
"""
Design optimal system architecture.
Extended thinking helps:
- Evaluate architectural patterns
- Consider scalability
- Plan for evolution
- Balance trade-offs
"""
```
---
## Integration Example: All Features Together
```python
# User: "I need to test my new navigation algorithm comprehensively"
# 1. PROMPT: User selects from available prompts
prompts = await list_available_prompts(category="benchmark")
# User selects: "benchmark-algorithm"
# 2. EXTENDED THINKING: Plan the benchmarking approach
plan = await sample_llm(
prompt="Design a comprehensive benchmark strategy for navigation algorithm...",
enable_extended_thinking=True # Give Claude time to think deeply
)
# 3. RESOURCE: Claude reads relevant docs and benchmarks
api_docs = await read_resource("docs://api/tools")
benchmark_spec = await read_resource("benchmarks://standard/nav2")
# 4. SAMPLING: Generate optimal test scenarios
test_scenarios = await sample_llm(
prompt=f'''Given {benchmark_spec} and algorithm characteristics,
design 30 diverse test scenarios that will reveal strengths and weaknesses...''',
max_tokens=2000
)
# 5. Execute tests with intelligent monitoring
results = await execute_tests_with_sampling(
scenarios=test_scenarios,
adaptive_difficulty=True, # Use sampling to adjust difficulty
intelligent_monitoring=True # Use sampling to watch for interesting patterns
)
# 6. EXTENDED THINKING: Comprehensive analysis
analysis = await sample_llm(
prompt=f'''Analyze these test results comprehensively: {results}
Consider:
- Performance patterns
- Failure modes
- Comparison to baseline
- Statistical significance
- Production readiness
- Specific recommendations
''',
enable_extended_thinking=True # Deep analysis
)
# 7. RESOURCE: Store results for future reference
await write_resource(
"tests://results/algorithm-v2-benchmark",
content=results
)
# 8. SAMPLING: Generate executive summary
summary = await sample_llm(
prompt=f"Create executive summary of {analysis} for non-technical stakeholders",
max_tokens=300
)
# User gets: Comprehensive benchmark with deep insights, all automated
```
---
## Success Criteria
- [ ] 20+ useful prompts covering common tasks
- [ ] 30+ resources exposing docs, state, configs
- [ ] Sampling used for 5+ intelligent decision-making scenarios
- [ ] Extended thinking improves complex problem solving by >50%
- [ ] Users report prompts help them discover capabilities
- [ ] Claude can answer questions by reading resources
- [ ] System makes intelligent autonomous decisions
- [ ] Complex problems solved more effectively
---
## Implementation Priority
**Week 1-2 (Foundation)**:
1. Core prompts (setup, debug, benchmark) - 5.11.1-5.11.4
2. Documentation resources - 5.11.9
3. Basic sampling for path planning - 5.11.17
**Week 3-4 (Intelligence)**:
4. World/model resources - 5.11.10-5.11.11
5. Runtime state resources - 5.11.12
6. Sampling for test generation - 5.11.18
7. Extended thinking for world generation - 5.11.24
**Week 5+ (Advanced)**:
- Remaining prompts and resources
- Advanced sampling scenarios
- Extended thinking for complex optimizations
---
**These features transform the MCP server from a tool provider into an intelligent agent that can guide, reason, and make autonomous decisions.** 🤖🧠✨
---
## Documentation Requirements
Each enhancement must include:
1. **Parameter Documentation**
- Description
- Valid range/options
- Default value and behavior
- Use cases
- Examples
2. **Code Examples**
- Basic usage
- Advanced usage
- Common combinations
3. **Migration Guide**
- How to adopt new features
- Breaking changes (none expected)
4. **Testing Guide**
- How to verify functionality
- Expected behavior
---
## Related Documents
**Phase 4 World Generation Enhancements:**
- `PHASE4_NICE_TO_HAVE_OPTIONS.md` - Detailed specifications
- `PHASE4_OPTIONS_QUICK_REFERENCE.md` - Quick lookup guide
- `PHASE_4_WORLD_GEN.md` - Base Phase 4 implementation
**All Phase Enhancements:**
- `PHASE_ENHANCEMENTS_ANALYSIS.md` - Comprehensive analysis (95+ enhancements)
- `PHASE_1_SETUP.md` - Base Phase 1 implementation
- `PHASE_2_INFRASTRUCTURE.md` - Base Phase 2 implementation
- `PHASE_3_CONTROL.md` - Base Phase 3 implementation
- `PHASE_6_TESTING.md` - Base Phase 6 implementation
---
## Next Steps
### Recommended Implementation Order
**Sprint 1-2 (Quick Wins - Developer Experience)**:
1. Pre-commit hooks (Module 5.6)
2. Development container (Module 5.6)
3. Debug mode (Module 5.7)
4. Mock mode (Module 5.7)
5. Improved error messages (Module 5.8)
**Sprint 3-4 (Production Foundations)**:
1. CI/CD pipelines (Module 5.6)
2. Health checks (Module 5.7)
3. Monitoring/metrics (Module 5.7)
4. Visual test reports (Module 5.9)
**Sprint 5-6 (Research Features - Phase 4)**:
1. Reproducible seeds (Module 5.1)
2. Extended materials (Module 5.2)
3. Environmental effects (Module 5.3)
**Sprint 7+ (Advanced Features)**:
1. Multi-robot coordination (Module 5.8)
2. Advanced testing (Module 5.9)
3. Additional Phase 4 enhancements (Modules 5.4, 5.5)
---
**Estimated Completion**: 4-6 weeks (can be done incrementally)
**Priority**: MEDIUM-LOW (All base phases should complete first)
**Status**: 🔵 Not Started
**Total Enhancements**: 145+ across 9 modules
**Note:** This entire phase is optional. All base phases (1-4, 6) provide complete, functional implementations. These 145+ enhancements add:
- Developer experience improvements (Modules 5.6, 5.9)
- Production reliability features (Module 5.7)
- Advanced robotics capabilities (Module 5.8)
- Research-grade simulation features (Modules 5.1-5.5)
Implement based on specific project needs and priorities.
