Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control

2026-09-15

AI Summary

研究通过去中心化对象控制方法解决多机器人协作搬运不同尺寸、重量和形状物体的问题，实现无需任务重设计的抓取与运输。

Abstract

We study cooperative multi-humanoid pickup and transport of objects with varying size, weight, and geometry, requiring robot teams of different sizes. Our approach uses decentralized object-centric control, where each humanoid is assigned a local attachment region on the shared object and learns to realize pickup and transport through gripperless bimanual pinching. This attachment-based interface provides a common control abstraction spanning single-robot pickup, cooperative multi-robot transport, and robot-to-robot handover, without per-task redesign. We find that policies trained only on single-robot pickup already transfer nontrivially to cooperative settings, suggesting that this abstraction captures much of the structure needed for coordination. At the same time, explicit multi-robot training further improves performance, showing that shared-object coupling introduces coordination dynamics that are beneficial to learn directly. We validate the approach in simulation across varying team sizes and object geometries, and demonstrate sim-to-real transfer on hardware, where the learned controllers enable real humanoids to perform cooperative manipulation tasks.

Problem

• multi-humanoid
• pickup and transport
• decentralized control
• object-centric
• cooperative manipulation

Innovation

• decentralized object-centric control
• gripperless bimanual pinching
• attachment-based interface
• multi-robot coordination