Planning for Muscular and Peripersonal-Space Comfort During Human-Robot Forceful Collaboration

Abstract

This work has been accepted and will appear in the 2018 IEEE-RAS International Conference on Humanoid Robots (HUMANOID 2018).This paper presents a planning algorithm designed to improve cooperative robot behaviour concerning human comfort during forceful human-robot physical interaction. Particularly, we are interested in planning for object grasping and positioning ensuring not only stability against the exerted human force but also empowering the robot with capabilities to address and improve human experience and comfort. Herein, comfort is addressed as both the muscular activation level required to exert the cooperative task, and the human spatial perception during the interaction, namely, the peripersonal space. By maximizing both comfort criteria, the robotic system can plan for the task (ensuring grasp stability) and for the human (improving human comfort). We believe this to be a key element to achieve intuitive and fluid human-robot interaction in real applications. Real HRI drilling and cutting experiments illustrated the efficiency of the proposed planner in improving overall comfort and HRI experience without compromising grasp stability. * First two authors contributed equally to this work.

Human Comfortability: Integrating Ergonomics and Muscular-Informed Metrics for Manipulability Analysis During Human-Robot Collaboration

“…To make both muscular-informed and ergonomics measures compatible, 2 we analyze (5)-(6) w.r.t. the most positive point in the workspace.…”

“…1-2 where a robot positions a board for the human to apply a task-specific force. The robot chooses the pose to optimize human comfort [2], [3]. In this context, increasing attention has been given to the control and planning of robot actions based on either biomechanics aspects (in terms of muscular activity) or ergonomics (in terms of industrial-standard postural assessment).…”

“…In this context, Peternel et al [3], [8] have presented excellent results focusing on muscular-activity-based fatigue estimation and adaptation through robot-tool attitude control. Using similar muscular-activity assessment, our previous work [2] presented an optimization-based pHRC planner that uses human muscle-space to decide on a robot grasp and object positioning that maximizes muscular comfort (and peripersonal space). One major drawback of such methods, emphasized in [2], is the computation time required to explore the task-tomuscle-space redundant mapping and the task-space itself, i.e., the freedom of where to position the shared object.…”

Caring About the Human Operator: Haptic Shared Control for Enhanced User Comfort in Robotic Telemanipulation

“…Then, we compute the corresponding W value assuming that the user naturally chooses the most comfortable configuration of the elbow when presented with a set of options. To do so, we choose the inverse kinematics solution corresponding to the ψ value leading to the least discomfort between the possible solutions (lowest W), similarly to what was done in [25]. This assumption only affects the swivel angle of the elbow and no other joint angle.…”

“…They relied on musculoskeletal simulations to train a Contextual Ergonomics Model through a probabilistic supervised learning Gaussian process. Chen et al [25] modeled the human arm as a 7-degrees-of-freedom (7-DoF) robotic manipulator, and its muscular effort was derived based on the estimated value of the joint torques. Similarly, Peternel et al [26] estimated muscular effort from torques during co-manipulation tasks.…”