Building robotic agents capable of operating across diverse environments and object types remains a significant challenge, often requiring extensive data collection. This is particularly restrictive in robotics, where each data point must be physically executed in the real world. Consequently, there is a critical need for alternative data sources for robotics and frameworks that enable learning from such data. In this work, we present Point Policy, a new method for learning robot policies exclusively from offline human demonstration videos and without any teleoperation data. Point Policy leverages state-of-the-art vision models and policy architectures to translate human hand poses into robot poses while capturing object states through semantically meaningful key points. This approach yields a morphology-agnostic representation that facilitates effective policy learning. Our experiments on 8 real-world tasks demonstrate an overall 75% absolute improvement over prior works when evaluated in identical settings as training. Further, Point Policy exhibits a 74% gain across tasks for novel object instances and is robust to significant background clutter. Videos of the robot are best viewed atthis https URL.
View on arXiv@article{haldar2025_2502.20391,
title={ Point Policy: Unifying Observations and Actions with Key Points for Robot Manipulation },
author={ Siddhant Haldar and Lerrel Pinto },
journal={arXiv preprint arXiv:2502.20391},
year={ 2025 }
}