学习多仿人机器人拾取与运输：基于去中心化物体中心控制
Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control
- Oregon State University（俄勒冈州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出去中心化物体中心控制方法，通过局部附着区域与双臂捏合，实现多仿人机器人协作拾取运输，并验证了从单机器人到多机器人的迁移及仿真到现实的可行性。
AI中文摘要：
我们研究了合作式多仿人机器人拾取和运输不同大小、重量和几何形状的物体，这需要不同规模的机器人团队。我们的方法采用去中心化的物体中心控制，其中每个仿人机器人被分配共享物体上的一个局部附着区域，并学习通过无夹爪的双臂捏合来实现拾取和运输。这种基于附着区域的接口提供了一种通用的控制抽象，涵盖了单机器人拾取、合作式多机器人运输以及机器人间交接，无需针对每个任务重新设计。我们发现，仅通过单机器人拾取训练的策略已经能够非平凡地迁移到合作场景，这表明该抽象捕获了协调所需的大部分结构。同时，显式的多机器人训练进一步提高了性能，表明共享物体的耦合引入了直接学习有益的协调动态。我们在模拟中验证了该方法在不同团队规模和物体几何形状下的有效性，并在硬件上展示了从仿真到现实的迁移，其中学习到的控制器使真实仿人机器人能够执行合作操作任务。
英文摘要：
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.
