SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

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 sin

Read original source ↗ Open in workspace

recordType

paper

region

Global