Grasp Point Optimization for Unknown Object Manipulation in Hand Task

In order to realize in-hand manipulation of unknown objects, we introduce an extension to our previously developed manipulation framework, such that long manipulation sequences, involving finger regrasping, become feasible. To this end, we propose a novel feedback controller, which searches for locally optimal contact points (suitable for regrasping), employing an online exploration process on the unknown object surface. The method autonomously estimates and follows the gradient of a smooth objective function. More concretely, we propose to dynamically switch between manipulability and grasp stability depending on the grasp stability level. Physics-based simulation experiments involving artificial noise to model real-world sensor readings, prove the feasibility of our approach, rotating an object while multiply readjusting the grasp configuration with all fingers in turn.