InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills

2026-03-08

AI Summary

Existing approaches to humanoid robot control struggle to achieve high-precision human-object interaction. To address this challenge, this work proposes InterReal, a unified physics-based imitation learning framework that integrates physical simulation, imitation learning, and reinforcement learning. The framework introduces two key innovations: a hand-object contact-constrained action augmentation strategy and a meta-policy-driven automatic reward learning mechanism. These components jointly enhance policy robustness under perturbations and improve sample efficiency. Evaluated on box-carrying and box-pushing tasks, InterReal achieves state-of-the-art tracking accuracy and task success rates in simulation. Furthermore, real-world deployment on the Unitree G1 humanoid robot demonstrates the framework’s effectiveness and robustness in physical environments.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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 signals to the low-level reinforcement learning objective, which enables more effective learning of interactive policies. Experiments on HOI tasks of box-picking and box-pushing demonstrate that InterReal achieves the best tracking accuracy and the highest task success rate compared to recent baselines. Furthermore, we validate the framework on the real-world robot Unitree G1, which demonstrates its practical effectiveness and robustness beyond simulation.

Problem

• humanoid robots
• human-object interaction
• interactive skills
• real-world deployment
• imitation learning

Innovation

• physics-based imitation learning
• human-object interaction
• reward shaping
• motion data augmentation
• real-world robot control