Colloquium: Robotic In-hand Manipulation with Push and Support Method

28 April 2017

In-hand manipulation is one of the distinctive skills in anthropomorphic hands. It is a process in which fingers push the object to generate expected manipulations. Although lots of research has been done on this topic, it is still a challenge in robotics. This research focuses on manipulating an 'unknown' object with an anthropomorphic robotic hand.

In this research, in-hand manipulation is transferred into a process where a push finger pushes an unknown object to roll onto an elastic surface (support fingers). The object and the elastic surface are treated as one black box system where action commands are sent as input and the observed visual-haptic feedback is output. Based on this concept, push and support models are proposed, including fixed support model, spring support model, and hybrid support model.

With these models, a process called haptic exploration is proposed, in which the robot slightly pushes the ob-ject in different directions and estimates the interaction state from haptic feedback. To verify the feasibil-ity of the proposed method, in-hand manipulation experiments have been conducted successfully on a real anthropomorphic hand platform. Furthermore, reinforcement Learning (RL) has been adopted to learn proper push commands through interacting with an in-hand manipulation simulator, which is con-structed with Radial Basis Function Networks (RBFNs) and trained by real manipulation data. Finally, learning experiments have been conducted based on different rewards: visual only rewards (unimodal) and visual-haptic rewards (multimodal). The experimental results demonstrate that our learning method is feasible; moreover, the use of multimodal rewards speeds up the learning process compared to the result from the use of unimodal rewards.

Friday, 28. April 2017, 14:00, Informatik, Room D-334

Junhu He, PhD candidate at group TAMS, University of Hamburg