High precision control of soft robots is challenging due to their stohcastic behavior and material-dependent nature. While RL has been applied in soft robotics, achieving precision in task execution is still a long way off. Traditionally, RL requires substantial data for convergence, often obtained from a training environment. Yet, despite exhibiting high accuracy in the training environment, RL-policies often fall short in reality due to the training-to-reality gap, and the performance is exacerbated by the stochastic nature of soft robots. This study paves the way for the implementation of RL for soft robot control to achieve high precision in task execution. Two sample-efficient adaptive control strategies are proposed that leverage the RL-policy. The schemes can overcome stochasticity, bridge the training-to-reality gap, and attain desired accuracy even in challenging tasks, such as obstacle avoidance. In addition, deliberate and reversible damage is induced to the pneumatic actuation chamber, altering the soft robot's behavior to test the adaptability of our solutions. Despite the damage, desired accuracy was achieved in most scenarios without needing to retrain the RL-policy.

中文翻译：

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基于 RL 的自适应控制器，用于软机器人手臂的高精度到达

由于其随机行为和材料依赖性，软机器人的高精度控制具有挑战性。虽然强化学习已应用于软机器人领域，但实现任务执行的精确性仍有很长的路要走。传统上，强化学习需要大量数据才能收敛，这些数据通常从训练环境中获得。然而，尽管在训练环境中表现出很高的准确性，但由于训练与现实之间的差距，强化学习策略在现实中常常达不到要求，而且软机器人的随机性也加剧了其性能。这项研究为软机器人控制中强化学习的实施铺平了道路，以实现任务执行的高精度。提出了两种利用 RL 策略的样本有效自适应控制策略。这些方案可以克服随机性，弥合训练与现实的差距，即使在避障等具有挑战性的任务中也能达到所需的准确性。此外，还会对气动驱动室造成故意和可逆的损坏，从而改变软机器人的行为，以测试我们解决方案的适应性。尽管存在损坏，但在大多数情况下都可以达到预期的准确性，而无需重新训练 RL 策略。