Reinforcement Learning for High-dimensional Continuous Control in Biomechanics: An Intro to ArtiSynth-RL
| Autor: | Abdi, Amir H., Malakoutian, Masoud, Oxland, Thomas, Fels, Sidney |
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| Rok vydání: | 2019 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Neural control is an exciting mystery which we instinctively master. Yet, researchers have a hard time explaining the motor control trajectories. Physiologically accurate biomechanical simulations can, to some extent, mimic live subjects and help us form evidence-based hypotheses. In these simulated environments, muscle excitations are typically calculated through inverse dynamic optimizations which do not possess a closed-form solution. Thus, computationally expensive, and occasionally unstable, iterative numerical solvers are the only widely utilized solution. In this work, we introduce ArtiSynth, a 3D modeling platform that supports the combined simulation of multi-body and finite element models, and extended to support reinforcement learning (RL) training. we further use ArtiSynth to investigate whether a deep RL policy can be trained to drive the motor control of a physiologically accurate biomechanical model in a large continuous action space. We run a comprehensive evaluation of its performance and compare the results with the forward dynamics assisted tracking with a quadratic objective function. We assess the two approaches in terms of correctness, stability, energy-efficiency, and temporal consistency. Comment: Deep Reinforcement Learning Workshop NeurIPS 2019 |
| Databáze: | arXiv |
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