Authors: George Jiayuan Gao, Tianyu Li, Nadia Figueroa
Abstract: We propose an object-centric recovery policy framework to address the
challenges of out-of-distribution (OOD) scenarios in visuomotor policy
learning. Previous behavior cloning (BC) methods rely heavily on a large amount
of labeled data coverage, failing in unfamiliar spatial states. Without relying
on extra data collection, our approach learns a recovery policy constructed by
an inverse policy inferred from object keypoint manifold gradient in the
original training data. The recovery policy serves as a simple add-on to any
base visuomotor BC policy, agnostic to a specific method, guiding the system
back towards the training distribution to ensure task success even in OOD
situations. We demonstrate the effectiveness of our object-centric framework in
both simulation and real robot experiments, achieving an improvement of
$\textbf{77.7\%}$ over the base policy in OOD. Project Website:
https://sites.google.com/view/ocr-penn
Source: http://arxiv.org/abs/2411.03294v1
