Robotics: Science and Systems XIX
Structured World Models from Human Videos
Russell Mendonca, Shikhar Bahl, Deepak PathakAbstract:
In this paper, we tackle the problem of learning complex, general behaviors directly in the real world. We propose an approach for robots to efficiently learn manipulation skills using only a handful of real-world interaction trajectories from many different settings. Inspired by the success of learning from large-scale datasets in the fields of computer vision and natural language, our belief is that in order to efficiently learn, a robot must be able to leverage internet-scale, human video data. Humans interact with the world in many interesting ways, which can allow a robot to not only build an understanding of useful actions and affordances but also how these actions affect the world for manipulation. Our approach builds a structured, human-centric action space grounded in visual affordances learned from human videos. Further, we train a world model on human videos and fine-tune on a small amount of robot interaction data without any task supervision. We show that this approach of affordance-space world models enables different robots to learn various manipulation skills in complex settings, in under 30 minutes of interaction. Videos can be found at https://human-world-model.github.io
Bibtex:
@INPROCEEDINGS{Mendonca-RSS-23, AUTHOR = {Russell Mendonca AND Shikhar Bahl AND Deepak Pathak}, TITLE = {{Structured World Models from Human Videos}}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2023}, ADDRESS = {Daegu, Republic of Korea}, MONTH = {July}, DOI = {10.15607/RSS.2023.XIX.012} }