Hybrid Learning- and Model-Based Planning and Control of In-Hand Manipulation

This paper presents a hierarchical framework for planning and control of in-hand manipulation of a rigid object involving grasp changes using fully-actuated multifin-gered robotic hands. While the framework can be applied to the general dexterous manipulation, we focus on a more complex definition of in-hand manipulation, where at the goal pose the hand has to reach a grasp suitable for using the object as a tool. The high level planner determines the object trajectory as well as the grasp changes, i.e. adding, removing, or sliding fingers, to be executed by the low-level controller. While the grasp sequence is planned online by a learning-based policy to adapt to variations, the trajectory planner and the low-level controller for object tracking and contact force control are exclusively model-based to robustly realize the plan. By infusing the knowledge about the physics of the problem and the low-level controller into the grasp planner, it learns to successfully generate grasps similar to those generated by model-based optimization approaches, obviating the high computation cost of online running of such methods to account for variations. By performing experiments in physics simulation for realistic tool use scenarios, we show the success of our method on different tool-use tasks and dexterous hand models. Additionally, we show that this hybrid method offers more robustness to trajectory and task variations compared to a model-based method.

I. Introduction

To use a tool for its designed purpose, it often has to be held with a grasp that is different from the one for picking it up. To turn a nut using a wrench, for example, one would first pick up the wrench using the fingertips and then pull it closer to the palm while transitioning to power grasp so that a large force can be applied (Fig. 1). It is therefore necessary to change the grasp along with the object pose relative to the hand between picking up and using the tool.