Yuto Shibata, Kotaro Amaya, Kashu Yamazaki, Lalit Jayanti, Yoshimitsu Aoki, Mariko Isogawa, Katerina Fragkiadaki · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28523 · open-access PDF
A interação humano-humano exige que os robôs operem sob severa observação parcial: durante o contato físico próximo, o parceiro humano é ocultado, truncado ou fora do campo de visão do robô, causando que a estimativa de posição falhe e muitas vezes exigindo rastreamento externo ou estado humano privilegiado. Ao mesmo tempo, as occlusões mútuas pesadas introduzem erros nos dados de captura de movimento humano-humano usados para aprender. apresentamos REACT, um quadro para aprender políticas de interação humano-humano implantáveis a partir de demonstrações humano-humano.
Machine translation (Meta M2M100); the English original follows.
Humanoid-human interaction requires robots to operate under severe partial observability: during close physical contact, the human partner is occluded, truncated, or outside the robot's field of view, causing pose estimation to fail and often necessitating external tracking or privileged human state. At the same time, heavy mutual occlusions introduce errors into the human-human motion-capture data used for learning. We present REACT, a framework for learning deployable humanoid-human interaction policies from human-human demonstrations. REACT treats semantic contact structure as the key super
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