R3M: A Universal Visual Representation for Robot Manipulation

# 🌟 New model addition ## Model description 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 task success by over 20% compared to training from scratch and by over 10% compared to state-of-the-art visual representations like CLIP and MoCo. Furthermore, R3M enables a Franka Emika Panda arm to learn a range of manipulation tasks in a real, cluttered apartment given just 20 demonstrations. <!-- Important information --> ## Open source status * [x] the model implementation is available:(https://github.com/facebookresearch/r3m) * [x] the model weights are available: https://github.com/facebookresearch/r3m/blob/main/r3m/example.py * [x] who are the authors: @suraj-nair-1