Coaching manipulation insurance policies for humanoid robots with
various information enhances their robustness and generalization throughout duties and platforms. Nevertheless, studying solely from robotic demonstrations is labor-intensive, requiring costly tele-operated information
assortment which is tough to scale. This paper investigates a extra scalable information supply, selfish human demonstrations, to function cross-embodiment coaching information for robotic studying. We mitigate the embodiment hole between humanoids and people
from each the info and modeling views. We accumulate an selfish task-oriented dataset (PH2D) that’s instantly aligned with humanoid manipulation demonstrations. We then practice a human-humanoid conduct coverage, which we time period Human Motion
Transformer (HAT). The state-action area of HAT is unified for each people and humanoid robots and could be differentiably retargeted to robotic actions. Co-trained with smaller-scale robotic information, HAT instantly fashions humanoid robots and people as totally different embodiments with out extra supervision. We present that human information improves each generalization and robustness of
HAT with considerably higher information assortment effectivity.
† College of California, San Diego
‡ Carnegie Mellon College
