智能磁性微型机器人通过深度强化学习学习游泳
Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning
摘要：
游泳微型机器人越来越多地开发复杂的材料和动态形状，并预计在复杂的环境中，系统动力学是难以建模和微型机器人的位置控制是不直接实现的。深度强化学习是一种很有前途的自主开发鲁棒控制器的方法，用于创建智能微型机器人，该机器人可以调整其行为以在未表征的环境中运行，而无需对系统动力学建模。在这里，我们报告的智能螺旋磁性水凝胶microrobot的发展，使用软演员评论家强化学习算法自主推导出一个控制策略，允许microrobot游泳通过一个未表征的仿生流体环境下的控制下的时变磁场产生的三轴阵列的电磁铁。强化学习代理学习成功的控制策略，训练步骤少于100，000，展示了快速学习的样本效率。我们还证明了，我们可以微调的控制策略学习的强化学习代理通过拟合数学函数的学习策略的动作分布，通过回归。应用于微型机器人控制的深度强化学习可能会显著扩展下一代微型机器人的能力。
英文摘要：
Swimming microrobots are increasingly developed with complex materials and dynamic shapes and are expected to operate in complex environments in which the system dynamics are difficult to model and positional control of the microrobot is not straightforward to achieve. Deep reinforcement learning is a promising method of autonomously developing robust controllers for creating smart microrobots, which can adapt their behavior to operate in uncharacterized environments without the need to model the system dynamics. Here, we report the development of a smart helical magnetic hydrogel microrobot that used the soft actor critic reinforcement learning algorithm to autonomously derive a control policy which allowed the microrobot to swim through an uncharacterized biomimetic fluidic environment under control of a time varying magnetic field generated from a three-axis array of electromagnets. The reinforcement learning agent learned successful control policies with fewer than 100,000 training steps, demonstrating sample efficiency for fast learning. We also demonstrate that we can fine tune the control policies learned by the reinforcement learning agent by fitting mathematical functions to the learned policy's action distribution via regression. Deep reinforcement learning applied to microrobot control is likely to significantly expand the capabilities of the next generation of microrobots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
