Efficient Robot Learning from Diverse Data

The lack of large-scale clean data for learning has been a challenge that significantly hinders robots from developing superior level of autonomous intelligence. This urges the necessity to utilize diverse data in a more efficient way. This work approaches the challenge from four perspectives: efficient learning from expert demonstration, efficient dynamics modeling from in-the-wild videos, efficient learning from heuristics guidance, and adjustment for efficient deployment. We provide an overview of preliminary results in each area and outline proposed research on extracting controllable representation from data, aiming at efficient cross-embodiment learning, as well as learning from multi fidelity data.