Research Project Title:
Learning Generalizable Control Policies for Soft Robots from Self-Supervised Morphological Features
abstract:Current methods of automating the design and control process of soft robots, such as co-design, are computationally expensive. One more efficient approach to automating this process is learning a morphology-agnostic controller using expert collected data, wherein the controller acts based on the intrinsic sensory readings and extrinsic morphology. Deep-learning based approaches to learning such a controller are fully supervised and are fundamentally limited by highly imbalanced training datasets. This work aims to develop a more robust control policy for soft robots that leverages self-supervised learning in order to better learn feature representations from unlabeled data and adaptively guide the design optimization landscape, thereby enabling the policy to generalize to new morphologies.
About:
Through this SuperUROP project, I want to become more involved in the application of deep learning algorithms to robotics. I hope to apply knowledge from previous machine learning classes and research to an exciting new area of study.
