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Vision-Robotics Bridge Affordances from Human Videos as a Versatile Representation for Robotics Shikhar Bahl * 1,2 Russell Mendonca * 1 Lili Chen 2 Unnat Jain 1,2 Deepak Pathak 1 1 Carnegie Mellon University 2 Meta AI CVPR 2023 Paper arXiv Video Code (Coming Soon) Dataset (Coming Soon) Summary Given a scene, our approach (VRB) learns actionable representations for robot learning. VRB predicts contact points and a post-contact trajectory learned from human videos . Abstract Building a robot that can understand and learn to interact by watching humans has inspired several vision problems. However, despite some successful results on static datasets, it remains unclear how current models can be used on a robot directly. In this paper, we aim to bridge this gap by leveraging videos of human interactions in an environment centric manner. Utilizing internet videos of human behavior, we train a vi...
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