Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.

不受束缚的小型软机器人在微创手术、靶向药物输送和生物工程应用中具有广阔的应用前景，因为它们可以直接且无创地进入人体中狭窄且难以到达的空间。对于此类潜在的生物医学应用，机器人控制的适应性对于确保操作的连续性至关重要，因为任务环境条件显示出可以改变机器人运动和任务性能的动态变化。传统建模和控制方法的适用性进一步受限于小规模软机器人，因为它们的运动学具有几乎无限的自由度、制造过程中固有的随机可变性以及现实世界交互过程中的动态变化。为了解决动态变化的任务环境的控制器适应挑战，我们建议使用贝叶斯优化 (BO) 和高斯过程 (GPs) 的毫米级磁行走软机器人的概率学习方法。我们的方法通过找到步态控制器参数提供了一种数据高效的学习方案，同时使用少量的物理实验优化了行走的软机器人的步幅。为了演示控制器的适应性，我们在具有不同表面附着力和粗糙度以及中等粘度的任务环境中测试了机器人的行走步态，旨在代表未来机器人在人体内执行任务的可能条件。