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

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)