Hidden Markov Model Based Shared Autonomy for Grip Strength Regulation in sEMG Driven Robot Hand Control

The integration of robots into human environments is advancing rapidly, driven by the demand for systems that combine robot accuracy and repeatability with human flexibility and adaptability. In this context, human-centered manipulation applications must address uncertainties arising from human, robotic, and environmental factors. As a result, effective robotic manipulation requires both accurate pre-grasping motions and precise grip strength control, especially in tasks where robotic devices are remotely controlled to grip objects with fine, desired, and adjustable grip force. The present work tackles the challenges posed by uncertainties and non-ideal conditions in surface electromyography (sEMG)-driven human-in-the-loop (HITL) robot hand control applications. In this regard, a novel probabilistic shared autonomy framework for fine grip strength regulation is introduced, leveraging Hidden Markov Models (HMMs) applied to tactile data to encode the HITL grasping action into proper, probabilistically consistent phases. These phases are then exploited to modulate the level of shared autonomy between the human operator and the robot hand, enabling precise control over grip strength. The presented shared autonomy framework was evaluated under multiple experimental conditions, testing grip force regulation performance with a group of 10+10 intactlimb participants and a participant with amputation in both static (fixed hand-object configuration) and dynamic (pick-andplace and recipe preparation) grasping tasks, with differentiated goals inspired by real-world requirements. Moreover, to explore generalizability, experiments were conducted with both anthropomorphic and industrial robotic hands, properly equipped with tactile sensors. Experimental outcomes are supported by statistical analysis and show, for the considered sample, the effectiveness of the proposed shared autonomy control architecture in achieving fine, smooth, and controllable grip strength regulation with respect to the baseline case in absence of our approach.