From Solo to Ensemble: A Hierarchical Framework for Composable Multi-Agent Human-Object Interaction

Key points

• Physics-based human-object interaction has achieved robust single-agent manipulation skills, yet extending them to multi-agent cooperative tasks remains challenging.1
• Existing approaches typically adapt interaction policies through task-specific fine-tuning, which entangles low-level contact-rich execution with high-level coordination and limits reuse across object geometries, interaction types, and team sizes.1
• Specifically, we reinterpret teacher rollouts as object-oriented action supervision by extracting short-horizon object-proxy motions from executed trajectories, and distill task-specific teachers into a low-level skill operating in an Object-oriented Action Space.1
• This formulation shifts multi-agent HOI learning from direct contact-rich full-body control to compact object-level proxy-motion coordination.1

Sources (1)

• [1]From Solo to Ensemble: A Hierarchical Framework for Composable Multi-Agent Human-Object Interaction
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 11:24 AM

  We propose a hierarchical framework that converts a single-agent HOI policy into a reusable Object-oriented Motion Skill.

  Physics-based human-object interaction has achieved robust single-agent manipulation skills, yet extending them to multi-agent cooperative tasks remains challenging.

Extractive summary: sentences quoted from the sources.