Diffusion Marl Motion Planning

---
name: diffusion-marl-motion-planning
description: Generative multi-robot motion planning using diffusion modeling with Multi-Agent Reinforcement Learning guidance
platforms: [linux, macos, windows]
---

# Diffusion + MARL for Multi-Robot Motion Planning

## Core Methodology

This approach combines **Diffusion Modeling** with **Multi-Agent Reinforcement Learning (MARL)** guidance for scalable multi-robot motion planning in shared environments.

### 1. Diffusion-Based Trajectory Generation
- Uses diffusion models to generate feasible robot trajectories
- Captures complex inter-agent interactions
- Scalable to large numbers of robots

### 2. MARL Guidance Module
- Reinforcement learning agents guide diffusion process
- Optimizes for collision avoidance and efficiency
- Decentralized decision-making architecture

### 3. Key Advantages
- **Scalability**: Overcomes centralized planning limitations
- **Coordination**: Accounts for inter-agent dependencies
- **Feasibility**: Generates physically realistic trajectories

## Implementation Points

### Architecture
```python
class DiffusionMARLPlanner:
    def __init__(self, num_agents, diffusion_model, marl_policy):
        self.diffusion = diffusion_model  # Trajectory generator
        self.marl_agents = [marl_policy for _ in range(num_agents)]
        
    def plan_trajectories(self, initial_positions, goals):
        # Generate base trajectories via diffusion
        base_trajectories = self.diffusion.sample(
            initial_positions, goals
        )
        
        # MARL guidance for coordination
        guided_trajectories = []
        for i, agent in enumerate(self.marl_agents):
            guidance = agent.act(base_trajectories[i])
            guided_trajectories.append(
                self.apply_guidance(base_trajectories[i], guidance)
            )
        
        return guided_trajectories
```

### Key Components
1. **Diffusion model**: Denoising process for trajectory generation
2. **MARL policy**: Multi-agent coordination strategy
3. **Guidance integration**: RL-informed trajectory refinement

## Use Cases

- Multi-robot warehouse navigation
- Autonomous vehicle fleet coordination
- Drone swarm path planning
- Collaborative manipulation tasks
- Shared-space robot coordination

## Activation Keywords

- `diffusion MARL`, `multi-robot motion planning`, `generative trajectory`
- `MARL guidance diffusion`, `robot coordination`, `decentralized planning`
- `diffusion-based motion planning`, `multi-agent trajectory generation`

## Related Skills

- [[multi-agent-reinforcement-learning]] - MARL methodology
- [[diffusion-models]] - Diffusion modeling
- [[multi-robot-systems]], [[robotics-planning]]
- [[generative-models-motion]]

## Reference

arXiv:2606.00933 - "Generative Multi-Robot Motion Planning via Diffusion Modeling with Multi-Agent Reinforcement Learning Guidance" (Lee et al., 2026)