Adaptive Fingers Coordination for Robust Grasp and In-Hand Manipulation Under Disturbances and Unknown Dynamics

We present a control framework for achieving a robust object grasp and manipulation in hand. In-hand manipulation remains a demanding task as the object is never stable and task success relies on carefully synchronizing the fingers' dynamics. Indeed, fingers must simultaneously generate motion while maintaining contact with the object and, by staying within the hand's frame, ensuring that the object remains manipulable. These challenges are exacerbated once the hand gets disturbed or when the internal dynamics of the manipulated object are unknown, such as when it is filled with liquid moving during manipulation. We present a control strategy based on coupled dynamical systems (DSs), whereby the fingers move in synchronization using an intermediate dynamics responsible for coordinating fingers. To adapt to changes in forces due to model uncertainties and unexpected disturbances, we employ an adaptive torque-controller combined with a joint impedance regulator that guarantees high tracking accuracy while adapting to dynamic changes. We validate the approach in multiple experiments on 16-degrees-of-freedom robotic hand grasping and manipulating objects with different mass properties, e.g., uneven or varying mass distribution in a glass half-filled with water. We show that the robot can compensate for disturbances generated by internal dynamics and external perturbations. Additionally, we showcase how our controller, in conjunction with learning from human demonstration, provides a robust solution for more complicated manipulations such as finger gaiting.

I. Introduction

In-hand manipulation is the ability to reposition an object within a hand. Grasping and manipulating objects of more variety and complexity are best explored when using a robotic hand with multiple degrees of freedom [1]. Although securely grasping the object is an essential ingredient required prior to in-hand manipulation [2], the main challenges arise after the object is grasped. The hand must carefully reposition the object by synchronously modulating the object–finger contacts/interactions [3]. Performing this in a robust fashion is never easy and becomes even more convoluted in the presence of uncertain and inaccurate estimations of the system's mechanical properties [4]. In this article, we address the problem of ensuring robust in-hand manipulation when faced with a poor model of the object's dynamics, model imperfections, and external disturbances. Next, we describe 1) our approach to addressing this problem, 2) the main parts of our control framework and their novelty over existing ones, 3) the experimental validation procedure, and 4) summarize our contributions.