PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining
Garrett Thomas, Ching-An Cheng, Ricky Loynd, Felipe Vieira Frujeri, Vibhav Vineet, Mihai Jalobeanu, Andrey Kolobov
Abstract
A rich representation is key to general robotic manipulation, but existing approaches to representation learning require large amounts of multimodal demonstrations. In this work we propose PLEX, a transformer-based architecture that learns from a small amount of task-agnostic visuomotor trajectories and a much larger amount of task-conditioned object manipulation videos — a type of data available in quantity. PLEX uses visuomotor trajectories to induce a latent feature space and to learn task-agnostic manipulation routines, while diverse video-only demonstrations teach PLEX how to plan in the induced latent feature space for a wide variety of tasks. Experiments showcase PLEX’s generalization on Meta-World and SOTA performance in challenging Robosuite environments. In particular, using relative positional encoding in PLEX’s transformers greatly helps in low-data regimes of learning from human-collected demonstrations.
BibTeX
@inproceedings{
thomas2023plex,
title={{PLEX}: Making the Most of the Available Data for Robotic Manipulation Pretraining},
author={Garrett Thomas and Ching-An Cheng and Ricky Loynd and Felipe Vieira Frujeri and Vibhav Vineet and Mihai Jalobeanu and Andrey Kolobov},
booktitle={7th Annual Conference on Robot Learning},
year={2023},
url={https://openreview.net/forum?id=Q8BGLiWn2X}
}