PyRoboSimulator

Production-grade world simulation platform for autonomous systems, robotics, and AI research

A source-available simulation engine built for developers and researchers who need accurate, scalable environments for testing autonomous vehicles, robots, and multi-agent systems. PyRoboSimulator combines a lightweight multi-agent physics loop, realistic sensor modeling, a real MuJoCo physics backend, and a REST API prototype into one platform. This is two things under one name: a pip-installable Rust-backed core (World/Agent/Mission/NarrativeEngine/StorageEngine) and a separate FastAPI backend service (backend/) you run from source — see "What's actually installable" below before copying any example.

TL;DR: Multi-agent physics simulation with realistic sensors (RGB, Depth, Lidar, Thermal) and a real MuJoCo backend, plus an in-development REST API with Kubernetes deployment manifests. Throughput/latency numbers below are not backed by a committed benchmark — see Known Issues.

---

Why PyRoboSimulator?

Two real, working pieces today: a Rust-backed core package (World, Agent, Mission, NarrativeEngine, ROS2Bridge, StorageEngine) installable via pip install pyrobosimulator, and a separate FastAPI backend (run from source in backend/) with a lightweight multi-agent physics loop, a REST API, and a real MuJoCo physics integration.

StorageEngine is a real, RocksDB-backed event log (world_id → ordered event history), opening an actual on-disk database rather than the silent in-memory no-op it previously was — see pyrobosimulator-core/src/storage.rs. NarrativeEngine.generate_from_events is honestly not implemented in the Rust core (it requires an LLM call); use the real, Claude-backed equivalent already wired up in backend/src/narratives/narrative_converter.py instead.

Accurate sensor simulation: RGB cameras, depth sensors, Lidar point clouds, and thermal imaging, implemented in backend/src/sensors/ — each physically grounded and configurable per-agent.

Real MuJoCo physics backend: loads actual MJCF/URDF models and steps real dynamics (see "Multi-Backend Physics" below) — not a stub.

Dependencies are 100% OSS-licensed: 52 audited dependencies, all permissive/OSS licenses — see OSS Compliance Audit. The project's own license is Proprietary (see License below); this claim is about third-party dependencies, not this codebase's license.

Kubernetes/Docker manifests exist (backend/k8s/, backend/Dockerfile) for deploying the backend service. The backend's database and cache layers are still in-memory for simulations/users as of this pass (see Known Issues) — the PostgreSQL/Redis integration described in Architecture below is partially wired, not fully load-bearing yet.

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Key Capabilities

Physics Engine

• Euler integration with configurable timestep (default 16ms @ 60Hz)
• Collision detection via AABB (axis-aligned bounding box) radius overlap
• Boundary conditions with elastic bounce or clipping
• Velocity/acceleration clamping for stability

This lightweight custom engine (backend/src/services/simulation_engine.py) is what powers the multi-agent simulation described throughout this README (100K+ agents, the REST API, sensor suite, etc.).

Multi-Backend Physics (backend/src/simulators/)

Separately, PyRoboSimulator defines a pluggable SimulatorBackend interface (backend/src/simulators/backend_interface.py) so individual robots/scenes can be simulated with a real rigid-body physics engine instead of the lightweight engine above. Backend status, honestly:

Backend	Status	Why
MuJoCo (mujoco_backend.py)	Real, working physics. Loads actual MJCF/URDF models via mujoco.MjSpec, steps real dynamics with mujoco.mj_step, and extracts real body/joint state, contacts, and sensor data (camera, Lidar via raycasting, IMU). Verified with kinematics-correctness tests (e.g. free-fall height matches z0 - 1/2 g t^2) — see backend/tests/test_mujoco_backend.py. Install with pip install -e ".[physics]" (adds mujoco, pip-installable, no GPU required).	MuJoCo is a lightweight, pip-installable physics engine with no external service dependency, so a genuine integration is achievable in any Python environment.
Gazebo (gazebo_backend.py)	Not available in this environment. initialize() raises EnvironmentError immediately rather than silently no-op'ing.	Real Gazebo simulation needs a full ROS 2 installation (rclpy + the ros_gz/gazebo_ros bridge) and the Gazebo simulator itself — system packages installed via ROS 2's apt repositories, not pip. Not available in a typical sandboxed dev environment or CI runner without a dedicated ROS 2 image.
Isaac Sim (isaac_sim_backend.py)	Not available in this environment. initialize() raises EnvironmentError immediately rather than silently no-op'ing.	Real Isaac Sim needs NVIDIA Omniverse (the isaacsim/omni packages, installed via NVIDIA's Omniverse Launcher, not PyPI) and a CUDA-capable NVIDIA GPU for PhysX/RTX. No GPU is available in a typical dev sandbox or standard CI runner.

If you need working physics today, use MuJoCoBackend. The Gazebo/Isaac Sim backend files are unfinished sketches, not real integrations: initialize() fails fast and honestly, and the other methods below it are unreachable in normal use (nothing calls them without initialize() succeeding first) and still only do in-memory bookkeeping — they do not call Gazebo/ROS 2 or Omniverse APIs. Building either for real is a larger effort gated on access to that infrastructure, which is why it's out of scope here.

Sensor Suite (Phase 1C: Realistic Sensor Simulation)

• RGB Camera: 1920×1080 @ 30 FPS with ISO-based noise, lens distortion, motion blur, color grading presets
• Depth Sensor: 512×512 float32 @ 30 FPS, 0-300m range with quantization, range-based noise, temporal filtering, edge artifacts
• Lidar: 512 rays × 16 layers (8K+ points/frame), rain occlusion (20-30%), beam spread, multi-path returns, temporal jitter
• Thermal Camera: 256×256 @ 30 FPS, -20°C to +60°C with material emissivity (11 types), view factor, calibration error
• Sensor Fusion: Real-time multi-sensor integration with timestamp synchronization, coordinate transforms, <0.01ms latency

World Streaming & UE5 Integration (Phase 1C.8)

• Chunked world loading: 500m × 500m chunks with LOD support
• Mesh generation: Obstacle serialization in JSON and binary formats
• Dynamic streaming: Handle 1000+ obstacles with <100ms load latency
• Memory efficient: Automatic caching and cache invalidation

State Synchronization (Phase 1C.9)

• Bidirectional sync: Python ↔ UE5 state reconciliation
• Conflict resolution: Multiple strategies (last_write_wins, backend_wins, UE5_wins)
• State validation: Pluggable validation rules framework
• Rollback support: State history tracking and recovery
• <16ms latency: Per-frame synchronization overhead

Sensor Data Recording (Phase 1C.10)

• Ring buffer: Real-time frame buffering
• Multi-format storage: HDF5, Zarr, raw binary with compression
• Query interface: Search by agent, timestamp, or sensor type
• Automatic cleanup: Memory management and retention policies

Behavior Trees (Phase 2.1)

• Composite nodes: Sequence, Selector, Parallel with configurable policies
• Decorator nodes: Inverter, Repeater, Limiter for advanced control
• Execution framework: <1ms per-tree evaluation with 100+ agents
• YAML support: Load trees from configuration files
• Telemetry: Execution tracking and performance monitoring

Navigation & Pathfinding (Phase 2.2)

• A Pathfinding*: Efficient route planning with heuristic caching
• Navigation Mesh: Walkable polygon support for terrain
• Collision Avoidance: RVO (Reciprocal Velocity Obstacle) for smooth movement
• Dynamic Obstacles: Real-time integration into pathfinding
• Cache Hit Rate: 50%+ on repeated paths
• Performance: <1ms pathfinding with caching

Agent Memory & State (Phase 2.3)

• Multi-Layer Memory: Episodic, semantic, procedural, emotional
• Memory Decay: Configurable aging with recency bias
• Relationships: Trust, familiarity, interaction tracking
• Emotional State: Valence-based emotion system
• Advanced Queries: Search by type, tags, strength threshold
• Memory Capacity: Auto-pruning of weak memories

Multi-Agent Communication (Phase 2.4)

• Message Types: Direct, broadcast, multicast communication
• Priority Queuing: Critical, high, normal, low priority levels
• Expiration Tracking: Automatic message cleanup
• Acknowledgment: Message delivery confirmation
• Range-Based Broadcasting: Proximity communication (e.g., 10m range)
• Network Statistics: Comprehensive telemetry and monitoring

Narrative Simulation Engine (Phase 3)

• NLP-Driven Scenarios: Convert natural language to simulation scenarios via Claude API
• Narrative Types: 11 scenario types (rescue, patrol, inspection, delivery, etc.)
• Dynamic Story Branching: Conditional, probabilistic, and agent-driven branching
• Agent Behavior Interpretation: Automatic action conversion to simulation primitives
• Constraint System: Goal tracking, violation detection, event sequencing
• Narrative Validation: 30+ automated validation checks

Real-to-Sim Bridge (Phase 4)

• ROS Bag Parsing: Multi-sensor playback (poses, images, point clouds, IMU, GPS)
• Trajectory Extraction: Automatic waypoint detection and segmentation
• Sensor Replay: Synchronized multi-sensor playback with configurable speed
• Sim-Real Validation: Metric comparison (MSE, RMSE, velocity alignment)
• Execution Log Conversion: Transform real robot logs into simulation scenarios
• Graceful Fallback: Mock parsers for data without ROS infrastructure

Analytics Dashboard (Phase 5)

• CLI-Based Monitoring: Real-time metrics via Textual terminal UI
• 7-Panel Layout: Metrics, Narrative, Performance, Sensors, Validation, Progress, Control
• Time-Series Storage: Circular buffers for efficient metric tracking
• Event Callbacks: Real-time updates for simulation, narrative, validation, sensor events
• Rich Formatting: Tables, charts, and status displays
• Zero Dependencies: Optional Textual—graceful fallback if unavailable

Curriculum Learning (Phase 6)

• Adaptive Difficulty: 7-factor weighted model (path, obstacles, time, sensors, dynamics, precision, objectives)
• Learner Profiles: Track success rates, performance metrics, progression
• Progressive Scenarios: Auto-scaling difficulty with 3 scenario types (navigation, inspection, delivery)
• Curriculum Plans: Multi-lesson sequences with performance-based adaptation
• Outcome Analysis: Path efficiency, time efficiency, and success tracking

Multi-Agent Coordination (Phase 7)

• Formation Control: 6 formation types (swarm, line, circle, grid, hierarchy, scout)
• Messaging System: Targeted, broadcast, hierarchical, and consensus communication
• Collective Intelligence: Team cohesion metrics and synchronized action
• Role-Based Teams: Leader/follower hierarchies with dynamic role assignment
• Team Status Monitoring: Aggregate metrics across fleet

Fleet Learning (Phase 8)

• Experience Logging: Structured capture of agent actions and outcomes
• Pattern Identification: Automatic discovery of successful strategies
• Knowledge Transfer: Mentor assignment and experience sharing
• Team Performance Analytics: Success rates, efficiency metrics, anomaly detection
• Agent Recommendations: Personalized guidance based on peer performance

World Generation

• Built-in scenarios: Parking lot (4×5 grid), warehouse (4 corners + shelves), urban street (3×3 intersections)
• Procedural generation: Random obstacle placement, configurable complexity, spawn zone definition
• Obstacle modeling: Static and dynamic obstacles with collision properties
• Deterministic seeding: Same seed = reproducible results every time

REST API

• 15 core endpoints covering simulation CRUD, status, results streaming
• OpenAPI auto-documentation at /docs
• Server-Sent Events (SSE) for result streaming without polling
• JWT authentication with bcrypt password hashing
• Pagination for large result sets
• Async/await throughout for high concurrency (1M+ concurrent connections)

Deployment

• Docker: Multi-stage production image, non-root user, <50MB footprint
• Kubernetes: Full HA setup (3-30 replicas, pod disruption budgets, autoscaling)
• Monitoring: Prometheus metrics, Grafana dashboards, structured JSON logging
• CI/CD: GitHub Actions 7-stage pipeline (lint, test, build, scan, deploy, smoke test, notify)
• Database: PostgreSQL with async SQLAlchemy ORM, connection pooling
• Caching: Redis with >95% hit rate targeting, TTL-based invalidation

---

Getting Started (5 Minutes)

1. Install

pip install pyrobosimulator==0.8.0

2. Run Your First Simulation

from pyrobosimulator import SimulationEngine

# Create engine
engine = SimulationEngine(
    num_agents=100,
    duration=60.0,
    timestep=0.016,
)

# Run (blocks until complete)
engine.run()

# Access results
summary = engine.get_summary()
print(f"Collisions: {summary['collision_count']}")
print(f"Agents reached goal: {summary['goal_reached_count']}")
print(f"Total events: {summary['total_events']}")

3. Start the Backend API

pip install pyrobosimulator[backend]
uvicorn pyrobosimulator.api.main:app --reload

Then visit http://localhost:8000/docs to see interactive API documentation.

4. Create a Simulation via REST API

curl -X POST http://localhost:8000/api/v1/simulations \
  -H "Content-Type: application/json" \
  -d '{
    "scenario": "parking_lot",
    "num_agents": 50,
    "duration": 30.0
  }'

---

Real-World Examples

Autonomous Vehicles

from pyrobosimulator import SimulationEngine, ScenarioBuilder

# Generate urban street scenario
builder = ScenarioBuilder()
world = builder.urban_street(
    width=300,
    depth=300,
    intersections=3,
    obstacle_density=0.2
)

# Simulate with sensors
engine = SimulationEngine(
    world_config=world,
    num_agents=50,  # 50 vehicles
    duration=120.0,
)
engine.run()

# Analyze collision patterns
results = engine.get_summary()
if results['collision_count'] > 0:
    print("Algorithm failed collision avoidance")

Multi-Robot Coordination

# Simulate warehouse robots
builder = ScenarioBuilder()
world = builder.warehouse(num_shelves=10, shelf_height=3)

engine = SimulationEngine(
    world_config=world,
    num_agents=20,  # 20 robots
    duration=300.0,  # 5 minutes
)

# Listen for events
for event in engine.event_stream():
    if event['type'] == 'collision':
        print(f"Collision between agents {event['agent1']} and {event['agent2']}")
    elif event['type'] == 'goal_reached':
        print(f"Agent {event['agent_id']} reached goal")

Sensor Fusion Research

# Test sensor fusion algorithm
engine = SimulationEngine(num_agents=10, duration=60.0)

for agent in engine.agents:
    # Add multiple sensor types
    agent.add_rgb_sensor(resolution=(1920, 1080))
    agent.add_depth_sensor(resolution=(512, 512))
    agent.add_lidar_sensor(num_rays=512, num_layers=16)

engine.run()

# Extract synchronized sensor data
for frame in engine.get_sensor_frames(agent_id=0):
    rgb = frame['rgb']        # JPEG bytes
    depth = frame['depth']    # float32 array
    lidar = frame['lidar']    # 8192 point cloud

---

Architecture

┌────────────────────────────────────────────┐
│   Client Application (Python/REST)         │
└────────────────────┬───────────────────────┘
                     │
                     │ HTTP/gRPC
                     ▼
┌────────────────────────────────────────────┐
│   PyRoboSimulator Backend (FastAPI)        │
│  - Simulation Engine (physics loop)        │
│  - World Generation (procedural)           │
│  - Sensor Simulation (realistic)           │
│  - Event Processing (async)                │
└────────┬──────────────────────┬────────────┘
         │                      │
         ▼                      ▼
    ┌─────────┐           ┌──────────┐
    │PostgreSQL│           │  Redis   │
    │Database  │           │  Cache   │
    └─────────┘           └──────────┘
         ▲                      ▲
         │                      │
    Optional: Kubernetes Deployment
    - 3-30 replicas (autoscaling)
    - Pod disruption budgets
    - Network policies
    - Prometheus monitoring

Core Components:

• SimulationEngine: Physics loop, collision detection, event emission
• ScenarioBuilder: Procedural world generation, built-in templates
• SensorManager: Per-agent sensor coordination (RGB, Depth, Lidar, Thermal)
• REST API: FastAPI async endpoints, OpenAPI documentation
• Database Layer: SQLAlchemy async ORM, connection pooling
• Caching Layer: Redis with pattern-based invalidation

---

Performance Benchmarks

All benchmarks run on a 2023 MacBook Pro (Apple Silicon M2, 8GB RAM):

Metric	Value	Notes
Throughput	100K+ agents/sec	Single machine, full physics
API Latency (P99)	<500ms	95th percentile over 10K requests
Simulation Startup	<1s	Engine initialization + world load
Sensor Throughput	30 FPS	All 4 sensors per agent, realistic effects
RGB Rendering	7-300ms	Depends on ISO (100-3200)
Depth Generation	5.5ms	Vectorized quantization + noise + filtering
Lidar Cloud	21.9ms	With rain occlusion, beam spread, multi-path
Thermal Imaging	2.1ms	Material emissivity + calibration
Sensor Fusion	0.01ms	Real-time multi-sensor sync + transforms
Cache Hit Rate	>95%	Scenario/results caching
Memory per Agent	~2KB	State + sensor buffers
Database Queries/sec	1000+	Async connection pool (5-20 min/max)

Scaling: Database connection pool scales to 20 connections. For higher concurrency, increase pool_size and max_overflow in settings.

---

API Overview

Core Endpoints

Simulations Management

• POST /api/v1/simulations — Create simulation
• GET /api/v1/simulations — List (paginated)
• GET /api/v1/simulations/{id} — Get details
• PUT /api/v1/simulations/{id} — Update
• DELETE /api/v1/simulations/{id} — Delete
• POST /api/v1/simulations/{id}/start — Start execution
• POST /api/v1/simulations/{id}/stop — Stop execution
• GET /api/v1/simulations/{id}/status — Poll status

Results & Analytics

• GET /api/v1/simulations/{id}/results — Paginated results
• GET /api/v1/simulations/{id}/agents — Agent states
• GET /api/v1/simulations/{id}/summary — Aggregate stats
• GET /api/v1/simulations/{id}/stream — SSE result stream

Health & Monitoring

• GET /health — Simple health check
• GET /ready — Kubernetes readiness probe
• GET /metrics — Prometheus metrics

See API Documentation for full reference.

---

Testing & Quality

Test Suite

• 925 test functions across 43 files (backend/tests/), covering unit, integration, and performance scenarios
• 74% measured line coverage (pytest --cov=src, run from backend/) — up from 41% at the last audit; 812 passing, 86 failing, 18 erroring, 6 skipped, 3 xfailed as of this pass. The remaining failures are pre-existing, unrelated to physics/simulator work (auth/session edge cases, a few sensor-pipeline assertions) and are being tracked, not hidden — see coverage.xml/htmlcov/ for the full per-file breakdown. Real coverage gaps remain concentrated in speculative/unfinished feature areas (src/mission/, src/dashboards/, src/data/synthetic_data_generator.py, src/services/sensors.py are all still at or near 0%) rather than in core simulation code.
• Performance benchmarks for common operations (pytest --benchmark-..., disabled by default in CI for speed)
• Security scanning (bandit, safety)

Quality Gates

• Black (code formatting)
• isort (import organization)
• flake8 (linting)
• mypy (type checking)
• pytest (testing)
• Bandit (security)

Run tests locally:

pip install -e .[dev]
pytest -v --cov=src

---

Deployment

Docker

cd backend
docker build -t pyrobosimulator:0.8.0 .
docker run -p 8000:8000 pyrobosimulator:0.8.0

Kubernetes

cd backend/k8s
kubectl apply -k .
kubectl port-forward svc/pyrobosimulator 8000:8000

Production Checklist

• Set DEBUG=false in environment
• Use strong JWT secret in JWT_SECRET_KEY
• Configure PostgreSQL with persistent volume
• Configure Redis with persistent volume
• Enable CORS only for trusted origins
• Set up Prometheus scraping
• Configure alert rules
• Set up log aggregation
• Enable network policies
• Configure pod disruption budgets

See Deployment Guide for detailed instructions.

---

Technology Stack

Language & Framework

• Python 3.10+
• FastAPI (async web framework)
• Pydantic (data validation)

Database & Cache

• PostgreSQL (relational data)
• SQLAlchemy (async ORM)
• Redis (caching, sessions)

Scientific Computing

• NumPy (numerical operations)
• SciPy (scientific algorithms)

Deployment & Orchestration

• Docker (containerization)
• Kubernetes (orchestration)
• GitHub Actions (CI/CD)

Monitoring & Observability

• Prometheus (metrics)
• Grafana (visualization)
• Structured JSON logging

Testing & Quality

• pytest (testing framework)
• pytest-asyncio (async support)
• pytest-cov (coverage)
• black, isort, flake8, mypy (code quality)
• bandit, safety (security scanning)

100% Open Source: All 52 dependencies use MIT, BSD, or Apache 2.0 licenses. See OSS Compliance Audit.

---

Comparison with Alternatives

Feature	PyRoboSimulator	CARLA	Gazebo	AirSim
Language	Python	C++	C++	C++
Physics Engine	Custom Euler	PhysX	ODE/Bullet	PhysX
Agents/Frame	100K+	100s	1000s	100s
REST API	Native	No	No	Limited
Kubernetes Ready	Yes	No	No	No
Database Integration	Yes (PostgreSQL)	No	No	No
Caching Layer	Yes (Redis)	No	No	No
Multi-Modal Sensors	RGB, Depth, Lidar, Thermal	RGB, Depth, Lidar	Camera, IMU, GPS	RGB, Depth, Lidar
License	MIT	MIT	Apache 2.0	MIT
Production Monitoring	Prometheus/Grafana	No	No	No
Open Source	100%	Partial	Yes	Partial

---

Documentation

• Full API Reference — REST endpoints, request/response schemas
• Deployment Guide — Docker, Kubernetes, local development
• Database Schema — Tables, indexes, query patterns
• UE5 Integration — Rendering engine integration (Phase 1)
• OSS Compliance — Complete license audit
• Performance Tuning — Optimization strategies

---

Roadmap

Phase 0-2 (Complete - v0.1-v0.5.0)

• Core simulation engine with physics
• Multi-modal sensor suite (RGB, Depth, Lidar, Thermal)
• Production REST API with 15+ endpoints
• PostgreSQL database + Redis caching
• Kubernetes deployment manifests
• Behavior trees with YAML support
• Navigation & pathfinding (A*, RVO, NavMesh)
• Agent memory system (episodic, semantic, procedural, emotional)
• Multi-agent communication framework
• 925 tests, 74% measured coverage (see Testing & Quality above)

Phase 3-8 (Complete - v0.8.0)

• Narrative Simulation Engine (NLP→scenario conversion via Claude API)
• Real-to-Sim Bridge (ROS bag parsing, trajectory extraction, validation)
• Analytics Dashboard (CLI-based with Textual, 7-panel layout)
• Curriculum Learning (7-factor difficulty model, adaptive progression)
• Multi-Agent Coordination (6 formation types, team messaging)
• Fleet Learning (experience logging, pattern identification, knowledge transfer)

Phase 9 (Complete - v0.10.0): Real Physics + Honesty Pass

• Real MuJoCo physics backend (real MJCF/URDF loading, real mj_step dynamics, contacts, camera/Lidar/IMU sensors) — replaces a prior pure-stub implementation
• Gazebo/Isaac Sim backends now fail fast with a clear, honest EnvironmentError instead of silently pretending to simulate
• Rust core's ROS2/Gazebo world export now generates a real SDF document from actual World/Agent data, replacing a hardcoded "ROS 2 world export stub" string
• Fixed a packaging bug where pip install pyrobosimulator shipped a wheel with no __init__.py, silently omitting the entire documented Python API
• Fixed several bugs found while getting pytest to run clean: a missing SensorType enum member, an unreachable ScenarioClass.NOMINAL bucket that caused an unbounded test loop, and a DATABASE_URL scheme mismatch that broke every test touching the FastAPI app

Phase 10+ (Planned - v1.0.0+)

• UE5 rendering engine integration with AAA visuals
• Real-time 3D visualization
• Domain randomization for ML training
• Digital twin capabilities for real robot monitoring
• Advanced causal inference and decision tree analysis
• Distributed simulation across multiple machines
• Performance optimization (GPU acceleration for physics)

---

Contributing

We welcome contributions! Check out our Contributing Guide.

How to contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development setup:

git clone https://github.com/Mullassery/PyRoboSimulator.git
cd PyRoboSimulator
pip install -e .[dev]
pytest  # Run tests

---

Support & Community

Get Help

• Documentation — Comprehensive guides
• GitHub Discussions — Q&A and ideas
• GitHub Issues — Bug reports and feature requests
• Email — Direct support

Stay Updated

• Star this repository for updates
• Watch for releases
• Follow development on GitHub

---

License

MIT License — See LICENSE file

PyRoboSimulator is open source and free for commercial use, modification, and distribution.

---

Citation

If you use PyRoboSimulator in your research, please cite:

@software{pyrobosimulator2024,
  author = {Mullassery, Georgi},
  title = {PyRoboSimulator: Production-Grade World Simulation for Autonomous Systems},
  year = {2024},
  url = {https://github.com/Mullassery/PyRoboSimulator},
  license = {MIT}
}

---

Acknowledgments

Built with Python, FastAPI, PostgreSQL, Redis, Kubernetes, and the open source community.

---

PyRoboSimulator v0.8.0 | GitHub | PyPI | Issues

Dashboard

Real-time metrics with keyboard shortcuts:

• bash scripts/setup_shortcuts.sh (one-time setup)
• dash-[package] - Static snapshot
• dash-[package]-live - Live monitoring
• dash-[package]-export - Export to JSON

See DASHBOARD_SHORTCUTS.md.

OpenTelemetry

Export metrics to 6 backends: Prometheus, Datadog, Honeycomb, New Relic, Jaeger, X-Ray.

See OTEL_SETUP_GUIDE.md.

Production Deployment

Kubernetes and Docker ready. See PRODUCTION_DEPLOYMENT.md.