Learning Optimal Impedance Control During Complex 3D Arm Movements

Humans use their limbs to perform various movements to interact with an external environment. Thanks to limb's variable and adaptive stiffness, humans can adapt their movements to the external unstable dynamics. The underlying adaptive mechanism has been investigated, employing a simple planar...

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Published in:IEEE robotics and automation letters Vol. 6; no. 2; pp. 1248 - 1255
Main Authors: Naceri, Abdeldjallil, Schumacher, Tobias, Li, Qiang, Calinon, Sylvain, Ritter, Helge
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation
Control stability
Dynamics
End effectors
Force
Human stiffness learning
human-centered robotics
Machine learning
machine learning for robot control
model learning for control
modeling and simulating humans
Nonlinear control
Perturbation methods
Predictive control
Robot arms
Robots
Stiffness
Task analysis
Three-dimensional displays
ISSN:2377-3766, 2377-3766
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Abstract Humans use their limbs to perform various movements to interact with an external environment. Thanks to limb's variable and adaptive stiffness, humans can adapt their movements to the external unstable dynamics. The underlying adaptive mechanism has been investigated, employing a simple planar device perturbed by external 2D force patterns. In this work, we will employ a more advanced, compliant robot arm to extend previous work to a more realistic 3D-setting. We study the adaptive mechanism and use machine learning to capture the human adaptation behavior. In order to model human's stiffness adaptive skill, we give human subjects the task to reach for a target by moving a handle assembled on the end-effector of a compliant robotic arm. The arm is force controlled and the human is required to navigate the handle inside a non-visible, virtual maze and explore it only through robot force feedback when contacting maze virtual walls. By sampling the hand's position and force data, a computational model based on a combination of model predictive control and nonlinear regression is used to predict participants' successful trials. Our study shows that participants selectively increased the stiffness within the axis direction of uncertainty to compensate for instability caused by a divergent external force field. The learned controller was able to successfully mimic this behavior. When it is deployed on the robot for the navigation task, the robot arm successfully adapt to the unstable dynamics in the virtual maze, in a similar manner as observed in the participants' adaptation skill.
AbstractList Humans use their limbs to perform various movements to interact with an external environment. Thanks to limb's variable and adaptive stiffness, humans can adapt their movements to the external unstable dynamics. The underlying adaptive mechanism has been investigated, employing a simple planar device perturbed by external 2D force patterns. In this work, we will employ a more advanced, compliant robot arm to extend previous work to a more realistic 3D-setting. We study the adaptive mechanism and use machine learning to capture the human adaptation behavior. In order to model human's stiffness adaptive skill, we give human subjects the task to reach for a target by moving a handle assembled on the end-effector of a compliant robotic arm. The arm is force controlled and the human is required to navigate the handle inside a non-visible, virtual maze and explore it only through robot force feedback when contacting maze virtual walls. By sampling the hand's position and force data, a computational model based on a combination of model predictive control and nonlinear regression is used to predict participants' successful trials. Our study shows that participants selectively increased the stiffness within the axis direction of uncertainty to compensate for instability caused by a divergent external force field. The learned controller was able to successfully mimic this behavior. When it is deployed on the robot for the navigation task, the robot arm successfully adapt to the unstable dynamics in the virtual maze, in a similar manner as observed in the participants’ adaptation skill.
Author Li, Qiang
Ritter, Helge
Naceri, Abdeldjallil
Schumacher, Tobias
Calinon, Sylvain
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SubjectTerms Adaptation
Control stability
Dynamics
End effectors
Force
Human stiffness learning
human-centered robotics
Machine learning
machine learning for robot control
model learning for control
modeling and simulating humans
Nonlinear control
Perturbation methods
Predictive control
Robot arms
Robots
Stiffness
Task analysis
Three-dimensional displays
Title Learning Optimal Impedance Control During Complex 3D Arm Movements
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