Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing
Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their usefulness in demanding real-world applications such as search and rescue, construction, industrial automation, and room o...
Gespeichert in:
Hauptverfasser: | , , , , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Recently, quadrupedal locomotion has achieved significant success, but their
manipulation capabilities, particularly in handling large objects, remain
limited, restricting their usefulness in demanding real-world applications such
as search and rescue, construction, industrial automation, and room
organization. This paper tackles the task of obstacle-aware, long-horizon
pushing by multiple quadrupedal robots. We propose a hierarchical multi-agent
reinforcement learning framework with three levels of control. The high-level
controller integrates an RRT planner and a centralized adaptive policy to
generate subgoals, while the mid-level controller uses a decentralized
goal-conditioned policy to guide the robots toward these sub-goals. A
pre-trained low-level locomotion policy executes the movement commands. We
evaluate our method against several baselines in simulation, demonstrating
significant improvements over baseline approaches, with 36.0% higher success
rates and 24.5% reduction in completion time than the best baseline. Our
framework successfully enables long-horizon, obstacle-aware manipulation tasks
like Push-Cuboid and Push-T on Go1 robots in the real world. |
---|---|
DOI: | 10.48550/arxiv.2411.07104 |