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Reinforcement Learning and Synergistic Control of the ACT Hand

机译：ACT手的强化学习和协同控制

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摘要

Tendon-driven systems are ubiquitous in biology and provide considerable advantages for robotic manipulators, but control of these systems is challenging because of the increase in dimensionality and intrinsic nonlinearities. Researchers in biological movement control have suggested that the brain may employ “muscle synergies” to make planning, control, and learning more tractable by expressing the tendon space in a lower dimensional virtual synergistic space. We employ synergies that respect the differing constraints of actuation and sensation, and apply path integral reinforcement learning in the virtual synergistic space as well as the full tendon space. Path integral reinforcement learning has been used successfully on torque-driven systems to learn episodic tasks without using explicit models, which is particularly important for difficult-to-model dynamics like tendon networks and contact transitions. We show that optimizing a small number of trajectories in virtual synergy space can produce comparable performance to optimizing the trajectories of the tendons individually. The six tendons of the index finger and eight tendons of the thumb, each actuating four degrees of joint freedom, are used to slide a switch and turn a knob. The learned control strategies provide a method for discovery of novel task strategies and system phenomena without explicitly modeling the physics of the robot and environment.
机译：肌腱驱动的系统在生物学中无处不在，并为机械手提供了相当大的优势，但是由于尺寸和固有非线性的增加，控制这些系统具有挑战性。生物运动控制的研究人员建议，大脑可以通过在较低维的虚拟协同空间中表达肌腱空间来利用“肌肉协同作用”来使计划，控制和学习变得更加容易。我们采用的协同作用要尊重致动和感觉的不同约束，并在虚拟协同空间以及整个肌腱空间中应用路径积分强化学习。路径积分补强学习已成功用于扭矩驱动系统，无需使用显式模型即可学习情节任务，这对于像腱网络和接触过渡这样难以建模的动力学尤为重要。我们显示，优化虚拟协同空间中的少量轨迹可以产生与单独优化筋腱轨迹可比的性能。食指的六个肌腱和拇指的八个肌腱，各自可激活四个关节自由度，用于滑动开关和旋转旋钮。学习到的控制策略提供了一种用于发现新型任务策略和系统现象的方法，而无需明确建模机器人和环境的物理过程。

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