Abstract
Observing a human demonstrator manipulate objects provides a rich, scalable and inexpensive source of data for learning robotic policies. However, transferring skills from in-the-wild human videos to a robotic manipulator poses several challenges, not least a difference in action and observation spaces. In this work, we use unlabeled videos of humans solving a wide range of manipulation tasks to learn a task-agnostic reward function for robotic manipulation policies. Thanks to the diversity of this training data, the learned reward function sufficiently generalizes to image observations from a previously unseen robot embodiment and environment to provide a meaningful prior for directed exploration in reinforcement learning. The learned rewards are based on distances to a goal in an embedding space learned using a time-contrastive objective. By conditioning the function on a goal image, we are able to reuse one model across a variety of tasks. Unlike prior work on leveraging human videos to teach robots, our method, Human Offline Learned Distances (HOLD) requires neither a-priori data from the robot environment, nor a set of task-specific human demonstrations, nor a predefined notion of correspondence across morphologies, yet it is able to accelerate training of several manipulation tasks on a simulated robot arm compared to using only a sparse reward obtained from task completion.
Video summary
Supplementary video
HOLD_supplementary_video.mp4Bibtex
@article{alakuijala2023learning,
title={Learning Reward Functions for Robotic Manipulation by Observing Humans},
author={Alakuijala, Minttu and Dulac-Arnold, Gabriel and Mairal, Julien and Ponce, Jean and Schmid, Cordelia},
journal={2023 IEEE International Conference on Robotics and Automation (ICRA)},
year={2023},
}