Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration

TL;DR

This paper presents a method for training soft robotic hands to perform dexterous manipulation tasks by learning from human demonstrations and reinforcement learning, enabling complex object manipulations with low-cost hardware.

Contribution

It introduces a novel demonstration blending algorithm and extends guided policy search to enable soft robotic hands to learn complex tasks from human demonstrations.

Findings

Successfully learned motor skills for valve turning, abacus manipulation, and grasping.

Demonstrated generalization across multiple manipulation tasks.

Enabled dexterous manipulation with low-cost soft robotic hardware.

Abstract

Dexterous multi-fingered hands can accomplish fine manipulation behaviors that are infeasible with simple robotic grippers. However, sophisticated multi-fingered hands are often expensive and fragile. Low-cost soft hands offer an appealing alternative to more conventional devices, but present considerable challenges in sensing and actuation, making them difficult to apply to more complex manipulation tasks. In this paper, we describe an approach to learning from demonstration that can be used to train soft robotic hands to perform dexterous manipulation tasks. Our method uses object-centric demonstrations, where a human demonstrates the desired motion of manipulated objects with their own hands, and the robot autonomously learns to imitate these demonstrations using reinforcement learning. We propose a novel algorithm that allows us to blend and select a subset of the most feasible demonstrations to learn to imitate on the hardware, which we use with an extension of the guided policy search framework to use multiple demonstrations to learn generalizable neural network policies. We demonstrate our approach on the RBO Hand 2, with learned motor skills for turning a valve, manipulating an abacus, and grasping.