R3M: A Universal Visual Representation for Robot Manipulation. (arXiv:2203.12601v3 [cs.RO] UPDATED)

We study how visual representations pre-trained on diverse human video data
can enable data-efficient learning of downstream robotic manipulation tasks.
Concretely, we pre-train a visual representation using the Ego4D human video
dataset using a combination of time-contrastive learning, video-language
alignment, and an L1 penalty to encourage sparse and compact representations.
The resulting representation, R3M, can be used as a frozen perception module
for downstream policy learning. Across a suite of 12 simulated robot
manipulation tasks, we find that R3M improves …

arxiv representation robot robot manipulation