Simulating human lifting motions using fuzzy-logic control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Combined Mechanisms of Internal Model Control and Impedance Control under Force Fields
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A novel method for learning policies from constrained motion
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Robust constraint-consistent learning
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Behaviour generation in humanoids by learning potential-based policies from constrained motion
Applied Bionics and Biomechanics
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Opening a door, turning a steering wheel, and rotating a coffee mill are typical examples of human movements that are constrained by the physical environment. The constraints decrease the mobility of the human arm and lead to redundancy in the distribution of actuator forces (either joint torques or muscle forces). Due to this actuator redundancy, there is an infinite number of ways to form a specific arm trajectory. However, humans form trajectories in a unique way. How do humans resolve the redundancy of the constrained motions and specify the hand trajectory? To investigate this problem, we examine human arm movements in a crank-rotation task. To explain the trajectory formation in constrained point-to-point motions, we propose a combined criterion minimizing the hand contact force change and the actuating force change over the course of movement. Our experiments show a close matching between predicted and experimental data.