Advanced Features Guide - MuJoCo MCP v0.8.2

🚀 Enhanced System Overview

MuJoCo MCP has evolved into a comprehensive robotics simulation platform with enterprise-grade features for research, development, and production use.

New Advanced Capabilities

Advanced Control Algorithms - PID, trajectory planning, optimization-based control
Multi-Robot Coordination - Formation control, cooperative manipulation, task allocation
Sensor Feedback Systems - Closed-loop control with various sensor modalities
Physics Benchmarking - Performance, accuracy, and scalability testing
Reinforcement Learning Integration - Gymnasium-compatible RL environments
Enhanced Viewer Server - High-performance, reliable simulation server
Real-time Visualization - Advanced plotting and monitoring tools

📚 Advanced Control Algorithms

PID Controllers

from mujoco_mcp.advanced_controllers import PIDController, PIDConfig

# Create PID controller
config = PIDConfig(kp=5.0, ki=0.1, kd=0.5)
pid = PIDController(config)

# Use in control loop
target = 1.57  # 90 degrees
current = robot.get_joint_position(0)
command = pid.update(target, current)

Trajectory Planning

from mujoco_mcp.advanced_controllers import TrajectoryPlanner

planner = TrajectoryPlanner()

# Minimum jerk trajectory
start_pos = np.array([0, 0, 0])
end_pos = np.array([1, 0.5, 0])
positions, velocities, accelerations = planner.minimum_jerk_trajectory(
    start_pos, end_pos, duration=5.0
)

# Spline trajectory through waypoints
waypoints = np.array([[0, 0], [1, 1], [2, 0], [3, 1]])
times = np.array([0, 1, 2, 3])
traj = planner.spline_trajectory(waypoints, times)

Optimization-Based Control

from mujoco_mcp.advanced_controllers import OptimizationController

controller = OptimizationController(horizon=10)
optimal_action = controller.quadratic_programming_control(
    current_state, target_state, dynamics_function
)

🤖 Multi-Robot Coordination

Formation Control

from mujoco_mcp.multi_robot_coordinator import MultiRobotCoordinator

coordinator = MultiRobotCoordinator()

# Add robots
coordinator.add_robot("robot1", "franka_panda", {"manipulation": True})
coordinator.add_robot("robot2", "ur5e", {"manipulation": True})

# Start coordination
coordinator.start_coordination()

# Formation control task
task_id = coordinator.formation_control(
    robots=["robot1", "robot2"],
    formation_type="line",
    spacing=1.5
)

Cooperative Manipulation

# Cooperative grasping task
approach_positions = {
    "robot1": np.array([0.3, 0.1, 0.5]),
    "robot2": np.array([0.3, -0.1, 0.5])
}

task_id = coordinator.cooperative_manipulation(
    robots=["robot1", "robot2"],
    target_object="box",
    approach_positions=approach_positions
)

Task Allocation

The system automatically allocates tasks based on:

Robot capabilities
Current availability
Task priorities
Resource requirements

🔬 Sensor Feedback Systems

Multi-Sensor Setup

from mujoco_mcp.sensor_feedback import create_robot_sensor_suite, create_feedback_controller

# Create sensor suite for robot
sensor_manager = create_robot_sensor_suite("franka_panda", n_joints=7)
sensor_manager.start_sensing()

# Create feedback controller
controller = create_feedback_controller("franka_panda")

# Set control target
target_state = {"joint_position": np.array([0, -0.785, 0, -1.57, 0, 1.57, 0])}
controller.set_target(target_state)

# Control loop
while running:
    sensor_readings = sensor_manager.get_latest_readings()
    controller.update_state(sensor_readings)
    commands = controller.compute_control()
    # Apply commands to robot

Sensor Types Supported

Joint Sensors: Position, velocity, torque
IMU: Acceleration, gyroscope, orientation
Force/Torque: End-effector forces and torques
Contact: Surface contact detection
Proximity: Distance sensing

Sensor Fusion

from mujoco_mcp.sensor_feedback import SensorFusion

fusion = SensorFusion()
fusion.add_sensor("imu", SensorType.IMU, weight=1.0)
fusion.add_sensor("encoder", SensorType.JOINT_POSITION, weight=0.8)

fused_data = fusion.fuse_sensor_data(sensor_readings)

📊 Physics Benchmarking

Comprehensive Benchmark Suite

from benchmarks.physics_benchmarks import BenchmarkSuite

suite = BenchmarkSuite("results/")
results = suite.run_all_benchmarks(parallel=True)

# Analyze results
for result in results:
    print(f"{result.test_name}: {'PASS' if result.success else 'FAIL'}")
    if result.metrics:
        for metric, value in result.metrics.items():
            print(f"  {metric}: {value:.4f}")

Available Benchmarks

Simulation Stability - Energy conservation, numerical stability
Performance - FPS, CPU usage, memory consumption
Accuracy - Comparison with analytical solutions
Scalability - Performance with increasing complexity

Custom Benchmarks

from benchmarks.physics_benchmarks import PhysicsBenchmark

class CustomBenchmark(PhysicsBenchmark):
    def _execute_benchmark(self) -> BenchmarkResult:
        # Custom benchmark implementation
        return BenchmarkResult(
            test_name="Custom Test",
            success=True,
            execution_time=1.5,
            metrics={"custom_metric": 0.95}
        )

🧠 Reinforcement Learning Integration

Gymnasium-Compatible Environments

from mujoco_mcp.rl_integration import create_reaching_env, RLTrainer

# Create RL environment
env = create_reaching_env("franka_panda")
trainer = RLTrainer(env)

# Baseline evaluation
baseline_results = trainer.random_policy_baseline(num_episodes=10)

# Custom policy evaluation
def my_policy(observation):
    # Your policy implementation
    return env.action_space.sample()

policy_results = trainer.evaluate_policy(my_policy, num_episodes=10)

Available RL Tasks

Reaching: Move end-effector to target position
Balancing: Keep system balanced (cart-pole, humanoid)
Walking: Locomotion for quadrupeds and humanoids
Manipulation: Grasping and object manipulation

Custom RL Tasks

from mujoco_mcp.rl_integration import TaskReward, MuJoCoRLEnvironment

class CustomTaskReward(TaskReward):
    def compute_reward(self, obs, action, next_obs, info):
        # Custom reward function
        return reward
    
    def is_done(self, obs, info):
        # Custom termination condition
        return done

# Create custom environment
config = RLConfig(
    robot_type="custom_robot",
    task_type="custom_task",
    max_episode_steps=1000
)
env = MuJoCoRLEnvironment(config)
env.reward_function = CustomTaskReward()

🖥️ Enhanced Viewer Server

High-Performance Server

# Start enhanced server
python mujoco_viewer_server_enhanced.py --host localhost --port 8888

# Features:
# - Connection pooling (up to 50 concurrent connections)
# - Performance monitoring
# - Automatic resource cleanup
# - Graceful shutdown
# - Memory leak prevention
# - Enhanced error handling

Server Diagnostics

# Get server diagnostics
echo '{"type":"get_diagnostics"}' | nc localhost 8888

# Response includes:
# - Performance metrics (CPU, memory)
# - Connection statistics
# - Model information
# - Request rates

Load Balancing and Scaling

The enhanced server supports:

Connection pooling for high concurrency
Resource monitoring for performance optimization
Automatic cleanup of stale resources
Health checks for reliability

📈 Real-time Visualization

Real-time Plotting

from mujoco_mcp.visualization_tools import RobotStateMonitor

# Create monitor
client = MuJoCoViewerClient()
client.connect()
monitor = RobotStateMonitor(client)

# Start monitoring
monitor.start_monitoring("robot_model", update_rate=50.0)

# Show visualizations
monitor.show_joint_positions()  # Real-time joint plots
monitor.show_dashboard()        # Interactive dashboard

# Export and analyze
monitor.export_data("robot_data.json")
analysis = monitor.analyze_performance()

Interactive Dashboards

from mujoco_mcp.visualization_tools import InteractiveVisualizer

dashboard = InteractiveVisualizer()
dashboard.create_dashboard("Robot Performance Dashboard")

# Add data streams
dashboard.add_data_stream("joint_angles", "position", 1, 1)
dashboard.add_data_stream("velocities", "velocity", 1, 2)
dashboard.add_data_stream("forces", "force", 2, 1)

# Update and show
dashboard.update_data_stream("joint_angles", timestamp, joint_data)
dashboard.show_dashboard()

3D Trajectory Visualization

from mujoco_mcp.visualization_tools import TrajectoryVisualizer

viz = TrajectoryVisualizer()
viz.add_trajectory("planned", planned_positions)
viz.add_trajectory("actual", actual_positions)

# Create and show 3D plot
viz.create_3d_plot()
viz.show_plot()

# Animated visualization
viz.animate_trajectory("planned", speed=1.0)

🔧 Integration Examples

Complete Robotic System

import asyncio
from mujoco_mcp.multi_robot_coordinator import MultiRobotCoordinator
from mujoco_mcp.visualization_tools import RobotStateMonitor
from mujoco_mcp.sensor_feedback import create_robot_sensor_suite

async def main():
    # Initialize coordinator
    coordinator = MultiRobotCoordinator()
    
    # Add robots
    coordinator.add_robot("arm1", "franka_panda", {"manipulation": True})
    coordinator.add_robot("arm2", "ur5e", {"manipulation": True})
    
    # Start coordination
    coordinator.start_coordination()
    
    # Setup monitoring
    monitor = RobotStateMonitor(coordinator.viewer_client)
    monitor.start_monitoring("arm1")
    
    # Cooperative task
    task_id = coordinator.cooperative_manipulation(
        robots=["arm1", "arm2"],
        target_object="assembly_part",
        approach_positions={
            "arm1": np.array([0.4, 0.2, 0.3]),
            "arm2": np.array([0.4, -0.2, 0.3])
        }
    )
    
    # Monitor task execution
    while True:
        status = coordinator.get_task_status(task_id)
        if status in ["completed", "failed"]:
            break
        await asyncio.sleep(0.1)
    
    # Export results
    monitor.export_data("cooperative_task_data.json")
    
    # Cleanup
    coordinator.stop_coordination()

if __name__ == "__main__":
    asyncio.run(main())

RL Training with Visualization

from mujoco_mcp.rl_integration import create_reaching_env
from mujoco_mcp.visualization_tools import RobotStateMonitor

# Create RL environment
env = create_reaching_env("franka_panda")

# Setup monitoring
monitor = RobotStateMonitor(env.viewer_client)
monitor.start_monitoring(env.model_id)

# Training loop with visualization
for episode in range(100):
    obs, _ = env.reset()
    episode_reward = 0
    
    while True:
        action = agent.act(obs)  # Your RL agent
        obs, reward, terminated, truncated, info = env.step(action)
        episode_reward += reward
        
        if terminated or truncated:
            break
    
    print(f"Episode {episode}: Reward = {episode_reward:.2f}")
    
    # Periodic analysis
    if episode % 10 == 0:
        analysis = monitor.analyze_performance()
        print(f"Performance metrics: {analysis}")

# Export training data
monitor.export_data(f"rl_training_data.json")

📋 Best Practices

Performance Optimization

Use Enhanced Viewer Server for production deployments
Enable connection pooling for multiple clients
Monitor resource usage with built-in diagnostics
Clean up resources regularly to prevent memory leaks
Use appropriate update rates for real-time applications

Multi-Robot Systems

Plan task allocation based on robot capabilities
Implement collision avoidance for safe operation
Use formation control for coordinated movement
Monitor system performance for scalability

RL Development

Start with baseline policies to establish performance floor
Use sensor feedback for more realistic training
Implement custom reward functions for specific tasks
Monitor training progress with visualization tools

Debugging and Analysis

Use benchmarking suite to identify performance bottlenecks
Export simulation data for offline analysis
Implement custom metrics for domain-specific evaluation
Use real-time visualization for immediate feedback

🔗 API Reference

Core Modules

advanced_controllers - Control algorithms and trajectory planning
multi_robot_coordinator - Multi-robot coordination and task allocation
sensor_feedback - Sensor processing and closed-loop control
rl_integration - Reinforcement learning environments and utilities
visualization_tools - Real-time plotting and analysis tools

Benchmark Modules

physics_benchmarks - Comprehensive simulation benchmarking

Enhanced Components

mujoco_viewer_server_enhanced - High-performance viewer server

🚀 Next Steps

The enhanced MuJoCo MCP system provides a solid foundation for:

Research Projects - Advanced control, multi-robot systems, RL
Industrial Applications - Automation, quality control, testing
Educational Use - Robotics courses, simulation labs
Commercial Products - Robotic system development and validation

For advanced use cases, consider extending the system with:

Custom robot models and controllers
Domain-specific sensors and actuators
Specialized visualization tools
Integration with external systems

The modular architecture makes it easy to add new capabilities while maintaining compatibility with existing features.

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