Scalable Multi-Human Cooperative Manipulation via Commander-Executor Coordination

Key points

• To address this issue, we propose a hierarchical commander-executor framework that enables multiple agents to accomplish complex collaborative tasks in distinct ways.1
• Conditioned on the commander's high-level strategy and each agent's local observations, the executor learns a low-level physics-based policy through a solo-to-cooperative curriculum to realize coordinated human-object interactions.1
• The planned object trajectory provides a shared coordination target, while root positions and grasping instructions specify role-dependent execution cues.1
• We evaluate our method on a variety of object manipulation tasks, demonstrating its ability to execute long-horizon tasks that combine different types of collaboration and to scale from two-agent to multi-agent cooperation.1

Sources (1)

• [1]Scalable Multi-Human Cooperative Manipulation via Commander-Executor Coordination
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 01:34 PM

  Multi-human cooperative object manipulation is a highly challenging topic in humanoid control.

  To address this issue, we propose a hierarchical commander-executor framework that enables multiple agents to accomplish complex collaborative tasks in distinct ways.

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