Learning Deformable Object Manipulation from Expert Demonstrations



We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images as inputs, given expert demonstrations. Our method uses demonstrations in three different ways, and balances the trade-off between exploring the environment online and using guidance from experts to explore high dimensional spaces effectively. We test DMfD on a set of representative manipulation tasks for a 1-dimensional rope and a 2-dimensional cloth from the SoftGym suite of tasks, each with state and image observations. Our method exceeds baseline performance by up to 12.9% for state-based tasks and up to 33.44% on image-based tasks, with comparable or better robustness to randomness. Additionally, we create two challenging environments for folding a 2D cloth using image-based observations, and set a performance benchmark for them. We deploy DMfD on a real robot with a minimal loss in normalized performance during real-world execution compared to simulation (~6%).

Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) and IEEE Robotics and Automation Letters (RA-L), 2022.

Authors: Gautam Salhotra *, I-Chun (Arthur) Liu *, Marcus Dominguez-Kuhne, Gaurav S. Sukhatme.

* indicates equal contribution.

Also published in ICRA 2022 2nd Workshop on Representing and Manipulating Deformable Objects.

I-Chun (Arthur) Liu
I-Chun (Arthur) Liu
CS (Robotics & AI) PhD Student

My research interests are in imitation learning, deep reinforcement learning, and vision for robotic manipulation.