Learning to manipulate a whip with simple primitive actions - A simulation study

Moses C Nah; Aleksei Krotov; Marta Russo; Dagmar Sternad; Neville Hogan

doi:10.1016/j.isci.2023.107395

Learning to manipulate a whip with simple primitive actions - A simulation study

iScience. 2023 Jul 14;26(8):107395. doi: 10.1016/j.isci.2023.107395. eCollection 2023 Aug 18.

Authors

Moses C Nah¹, Aleksei Krotov², Marta Russo^{3

4}, Dagmar Sternad⁵, Neville Hogan^{1

6}

Affiliations

¹ Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
² Department of Bioengineering, Northeastern University, Boston, MA 02115, USA.
³ Department of Biology, Northeastern University, Boston, MA 02115, USA.
⁴ Department of Neurology, Policlinico Tor Vergata and the Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy.
⁵ Department of Biology, Department of Electrical and Computer Engineering, Department of Physics, Institute of Experiential Robotics, Northeastern University, Boston, MA 02115, USA.
⁶ Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Abstract

This simulation study investigated whether a 4-degrees-of-freedom (DOF) arm could strike a target with a 50-DOF whip using a motion profile similar to discrete human movements. The interactive dynamics of the multi-joint arm was modeled as a constant joint-space mechanical impedance, with values derived from experimental measurement. Targets at various locations could be hit with a single maximally smooth motion in joint-space coordinates. The arm movements that hit the targets were identified with fewer than 250 iterations. The optimal actions were essentially planar arm motions in extrinsic task-space coordinates, predominantly oriented along the most compliant direction of both task-space and joint-space mechanical impedances. Of the optimal movement parameters, striking a target was most sensitive to movement duration. This result suggests that the elementary actions observed in human motor behavior may support efficient motor control in interaction with a dynamically complex object.

Keywords: Engineering; Mechanical modeling; Robotics.

Grants and funding

R37 HD087089/HD/NICHD NIH HHS/United States