The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning

Paper Website

Masked Multimodal Learning (M3L) is a representation learning technique for reinforcement learning that targets robotic manipulation systems provided with vision and high-resolution touch.

Installation

Please install tactile_envs first. Then, install the remaining dependencies:

pip install -r requirements.txt

Training M3L

MUJOCO_GL='egl' python train.py --env tactile_envs/Insertion-v0

Training M3L (vision policy)

MUJOCO_GL='egl' python train.py --env tactile_envs/Insertion-v0 --vision_only_control True

Citation

If you find M3L useful for your research, please cite this work:

@article{sferrazza2023power,
  title={The power of the senses: Generalizable manipulation from vision and touch through masked multimodal learning},
  author={Sferrazza, Carmelo and Seo, Younggyo and Liu, Hao and Lee, Youngwoon and Abbeel, Pieter},
  year={2023}
}

References

This codebase contains some files adapted from other sources:

• vit-pytorch: https://github.com/lucidrains/vit-pytorch