Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration

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.

Accepted at International Conference on Intelligent Robots and Systems(IROS) 2016. Pdf file updated for stylistic consistency

References in corpus (2)

• End-to-End Training of Deep Visuomotor Policies
• Learning Contact-Rich Manipulation Skills with Guided Policy Search

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• Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning
• Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
• Learning-based Feedback Controller for Deformable Object Manipulation
• O2A: One-shot Observational learning with Action vectors
• Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression
• Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning
• Domain-Adversarial and Conditional State Space Model for Imitation Learning
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• Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects