Generating various goal-oriented movements via the flexible muscle model of the musculoskeletal system as fast and accurately as possible is a pressing problem, which is also the basis of most human adaptive behaviors, such as reaching, catching, interception, and pointing. This article focuses on the adaptive motion generation of fast goal-oriented motion on the musculoskeletal system by implementing the speed–accuracy tradeoff (SAT) in a hierarchical motion learning framework. First, we introduce Fitts’ Law into the modified basal ganglia circuit-inspired iterative decision-making model for achieving dynamic and adaptive decision making. Then, as a time constraint, the decision is decomposed into a series of supervised terms by the proposed striatal FSI-SPN interneuron circuit-inspired velocity modulator to implement the tradeoff smoothly on the musculoskeletal system. Finally, an improved policy gradient algorithm is suggested to generate the muscle excitations of the modulated motion via the proposed muscle co-contraction policy, which promotes general cooperation between flexor and extensor muscles. In experiments, a redundant musculoskeletal arm model is trained to perform the adaptive quick pointing movements. By combining the muscle co-contraction policy with SAT, our algorithm shows the most efficient training and the best performance in the adaptive motion generation among the other three popular reinforcement learning algorithms on the musculoskeletal model.

中文翻译：

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用于目标导向运动的分层运动学习与肌肉骨骼系统的速度和准确性权衡


通过肌肉骨骼系统的柔性肌肉模型尽可能快速、准确地产生各种目标导向的运动是一个紧迫的问题，这也是大多数人类适应性行为的基础，例如伸手、抓住、拦截和指向。本文重点关注通过在分层运动学习框架中实现速度与精度权衡（SAT），在肌肉骨骼系统上实现快速目标导向运动的自适应运动生成。首先，我们将菲茨定律引入改进的基底神经节电路启发的迭代决策模型中，以实现动态和自适应决策。然后，作为时间限制，该决策被所提出的纹状体 FSI-SPN 中间神经元电路启发的速度调制器分解为一系列监督项，以在肌肉骨骼系统上顺利实现权衡。最后，建议采用改进的策略梯度算法，通过所提出的肌肉共同收缩策略来生成调制运动的肌肉激励，从而促进屈肌和伸肌之间的一般合​​作。在实验中，训练冗余肌肉骨骼手臂模型来执行自适应快速指向运动。通过将肌肉协同收缩策略与 SAT 相结合，我们的算法在肌肉骨骼模型上的其他三种流行的强化学习算法中显示出最有效的训练和自适应运动生成的最佳性能。