Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems

TL;DR

This paper introduces a hybrid control framework for multi-robot systems in cluttered environments, using waypoints and curriculum-based RL to jointly optimize task and motion planning, improving success rates.

Contribution

It proposes a novel waypoint-based parameterization and a curriculum-based RL strategy for joint task and motion planning in multi-robot systems.

Findings

Improved task success over baselines in dense obstacle environments.

Effective joint optimization of task and motion planning.

Code available at https://github.com/UCSB-NLP-Chang/navigate-cluster

Abstract

Multi-robot control in cluttered environments is a challenging problem that involves complex physical constraints, including robot-robot collisions, robot-obstacle collisions, and unreachable motions. Successful planning in such settings requires joint optimization over high-level task planning and low-level motion planning, as violations of physical constraints may arise from failures at either level. However, jointly optimizing task and motion planning is difficult due to the complex parameterization of low-level motion trajectories and the ambiguity of credit assignment across the two planning levels. In this paper, we propose a hybrid multi-robot control framework that jointly optimizes task and motion planning. To enable effective parameterization of low-level planning, we introduce waypoints, a simple yet expressive representation for motion trajectories. To address the credit assignment challenge, we adopt a curriculum-based training strategy with a modified RLVR algorithm that propagates motion feasibility feedback from the motion planner to the task planner. Experiments on BoxNet3D-OBS, a challenging multi-robot benchmark with dense obstacles and up to nine robots, show that our approach consistently improves task success over motion-agnostic and VLA-based baselines. Our code is available at https://github.com/UCSB-NLP-Chang/navigate-cluster