InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills
Dayang Liang, Yuhang Lin, Xinzhe Liu, Jiyuan Shi, Yunlong Liu, Chenjia Bai

TL;DR
InterReal is a physics-based imitation learning framework that enables humanoid robots to learn and perform human-object interactions with high accuracy and robustness in real-world settings.
Contribution
The paper introduces a unified framework combining data augmentation, an automatic reward learner, and meta-policy guidance for improved human-object interaction learning.
Findings
Achieves superior tracking accuracy in HOI tasks
Attains higher success rates compared to recent baselines
Demonstrates effective real-world robot deployment
Abstract
Interaction is one of the core abilities of humanoid robots. However, most existing frameworks focus on non-interactive whole-body control, which limits their practical applicability. In this work, we develop InterReal, a unified physics-based imitation learning framework for Real-world human-object Interaction (HOI) control. InterReal enables humanoid robots to track HOI reference motions, facilitating the learning of fine-grained interactive skills and their deployment in real-world settings. Within this framework, we first introduce a HOI motion data augmentation scheme with hand-object contact constraints, and utilize the augmented motions to improve policy stability under object perturbations. Second, we propose an automatic reward learner to address the challenge of large-scale reward shaping. A meta-policy guided by critical tracking error metrics explores and allocates reward…
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Taxonomy
TopicsRobot Manipulation and Learning · Human Pose and Action Recognition · Social Robot Interaction and HRI
