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
Interaksi manusia-manusia membutuhkan robot untuk beroperasi di bawah pengamatan parsial yang parsial yang parah: selama kontak fisik yang dekat, pasangan manusia tertutup, dipotong, atau di luar bidang penglihatan robot, menyebabkan perkiraan pose gagal dan seringkali membutuhkan pelacakan eksternal atau kondisi manusia yang istimewa. Pada saat yang sama, tertutup mutual yang berat memperkenalkan kesalahan ke dalam data-data pengambilan gerakan manusia-manusia yang digunakan untuk belajar. Kami menyajikan REACT, kerangka kerja untuk mempelajari kebijakan interaksi manusia-manusia yang dapat diimplementasikan dari demonstrasi manusia-manusia.
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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