Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements
CoRR(2024)
摘要
The mimicking of human-like arm movement characteristics involves the
consideration of three factors during control policy synthesis: (a) chosen task
requirements, (b) inclusion of noise during movement execution and (c) chosen
optimality principles. Previous studies showed that when considering these
factors (a-c) individually, it is possible to synthesize arm movements that
either kinematically match the experimental data or reproduce the stereotypical
triphasic muscle activation pattern. However, to date no quantitative
comparison has been made on how realistic the arm movement generated by each
factor is; as well as whether a partial or total combination of all factors
results in arm movements with human-like kinematic characteristics and a
triphasic muscle pattern. To investigate this, we used reinforcement learning
to learn a control policy for a musculoskeletal arm model, aiming to discern
which combination of factors (a-c) results in realistic arm movements according
to four frequently reported stereotypical characteristics. Our findings
indicate that incorporating velocity and acceleration requirements into the
reaching task, employing reward terms that encourage minimization of mechanical
work, hand jerk, and control effort, along with the inclusion of noise during
movement, leads to the emergence of realistic human arm movements in
reinforcement learning. We expect that the gained insights will help in the
future to better predict desired arm movements and corrective forces in
wearable assistive devices.
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