Antagonistic muscle based robot control for physical interactions

Robots are ever more present in human environments and effective physical human-robot interactions are essential to many applications. But to a person, these interactions rarely feel biological or equivalent to a human-human interactions. It is our goal to make robots feel more human-like, in the hopes of allowing more natural human-robot interactions. In this paper, we examine a novel biologically-inspired control method, emulating antagonistic muscle pairs based on a nonlinear Hill model. The controller captures the muscle properties and dynamics and is driven solely by muscle activation levels. A human-robot experiment compares this approach to PD and PID controllers with equivalent impedances as well as to direct human-human interactions. The results show the promise of driving motors like muscles and allowing users to experience robots much like humans.

[1]  J. Salisbury,et al.  Active stiffness control of a manipulator in cartesian coordinates , 1980, 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[2]  N. Hogan.  Adaptive control of mechanical impedance by coactivation of antagonist muscles , 1984 .

[3]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation , 1984, 1984 American Control Conference.

[4]  Jack M. Winters,et al.  Analysis of Fundamental Human Movement Patterns Through the Use of In-Depth Antagonistic Muscle Models , 1985, IEEE Transactions on Biomedical Engineering.

[5]  E. Bizzi,et al.  Neural, mechanical, and geometric factors subserving arm posture in humans , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[6]  R. Shadmehr,et al.  Control of Equilibrium Position and Stiffness Through Postural Modules. , 1993, Journal of motor behavior.

[7]  Mark L. Nagurka,et al.  Dynamic and loaded impedance components in the maintenance of human arm posture , 1993, IEEE Trans. Syst. Man Cybern..

[8]  Brian R. Duffy,et al.  Anthropomorphism and the social robot , 2003, Robotics Auton. Syst..

[9]  Yoshiyuki Tanaka,et al.  Analysis of mechanical impedance in human arm movements using a virtual tennis system , 2004, Biological Cybernetics.

[10]  G. Giralt,et al.  Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Antonio Bicchi,et al.  An atlas of physical human-robot interaction , 2008 .

[12]  Alin Albu-Schäffer,et al.  Biomimetic motor behavior for simultaneous adaptation of force, impedance and trajectory in interaction tasks , 2010, 2010 IEEE International Conference on Robotics and Automation.

[13]  Stefan Schaal,et al.  Learning variable impedance control , 2011, Int. J. Robotics Res..

[14]  Martin Buss,et al.  Comparison of people's responses to real and virtual handshakes within a virtual environment , 2011, Brain Research Bulletin.

[15]  Konrad P. Körding,et al.  Toward Perceiving Robots as Humans: Three Handshake Models Face the Turing-Like Handshake Test , 2012, IEEE Transactions on Haptics.

[16]  Manuel G. Catalano,et al.  Variable impedance actuators: A review , 2013, Robotics Auton. Syst..

[17]  O. Schmitt.  The heat of shortening and the dynamic constants of muscle , 2017 .