Making Sense of Vision and Touch:
Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks
Michelle A. Lee*, Yuke Zhu*, Krishnan Srinivasan, Parth Shah,
Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg
Abstract: Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. However, it is non-trivial to manually design a robot controller that combines modalities with very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. We use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. We evaluate our method on a peg insertion task, generalizing over different geometry and clearances, while being robust to external perturbations. Results for simulated and real robot experiments are presented.
Preprint: available on arXiv