Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos

• URL: http://arxiv.org/abs/2602.13197v1
• Date: Fri, 13 Feb 2026 18:59:10 GMT
• Title: Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos
• Authors: Albert J. Zhai, Kuo-Hao Zeng, Jiasen Lu, Ali Farhadi, Shenlong Wang, Wei-Chiu Ma,
• Abstract summary: We tackle prehensile manipulation, in which tasks involve grasping an object before performing various post-grasp motions.<n>Human videos offer strong signals for learning the post-grasp motions, but they are less useful for learning the prerequisite grasping behaviors.<n>We present Perceive-Simulate-Imitate (PSI), a framework for training a modular manipulation policy using human video motion data.
• Score: 56.510263910611684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning. Here, we tackle prehensile manipulation, in which tasks involve grasping an object before performing various post-grasp motions. Human videos offer strong signals for learning the post-grasp motions, but they are less useful for learning the prerequisite grasping behaviors, especially for robots without human-like hands. A promising way forward is to use a modular policy design, leveraging a dedicated grasp generator to produce stable grasps. However, arbitrary stable grasps are often not task-compatible, hindering the robot's ability to perform the desired downstream motion. To address this challenge, we present Perceive-Simulate-Imitate (PSI), a framework for training a modular manipulation policy using human video motion data processed by paired grasp-trajectory filtering in simulation. This simulation step extends the trajectory data with grasp suitability labels, which allows for supervised learning of task-oriented grasping capabilities. We show through real-world experiments that our framework can be used to learn precise manipulation skills efficiently without any robot data, resulting in significantly more robust performance than using a grasp generator naively.

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