Integrating Brain-Machine Interfaces into non-clinical applications like robot motion control remains difficult - despite remarkable advancements in clinical settings. Specifically, EEG-based motor imagery systems are still error-prone, posing safety risks when rigid robots operate near humans. This work presents an alternative pathway towards safe and effective operation by combining wearable EEG with physically embodied safety in soft robots. We introduce and test a pipeline that allows a user to move a soft robot's end effector in real time via brain waves that are measured by as few as three EEG channels. A robust motor imagery algorithm interprets the user's intentions to move the position of a virtual attractor to which the end effector is attracted, thanks to a new Cartesian impedance controller. We specifically focus here on planar soft robot-based architected metamaterials, which require the development of a novel control architecture to deal with the peculiar nonlinearities - e.g., non-affinity in control. We preliminarily but quantitatively evaluate the approach on the task of setpoint regulation. We observe that the user reaches the proximity of the setpoint in 66% of steps and that for successful steps, the average response time is 21.5s. We also demonstrate the execution of simple real-world tasks involving interaction with the environment, which would be extremely hard to perform if it were not for the robot's softness.

翻译：将脑机接口集成到机器人运动控制等非临床应用中仍面临挑战——尽管在临床领域已取得显著进展。具体而言，基于脑电图（EEG）的运动想象系统仍易出错，当刚性机器人在人类附近作业时存在安全风险。本研究通过将可穿戴EEG与软体机器人物理性本体安全相结合，提出了一种实现安全高效运行的替代方案。我们提出并测试了一个流水线系统，该系统允许用户通过仅三个EEG通道测量的脑电波实时移动软体机器人末端执行器。借助新型笛卡尔阻抗控制器，鲁棒的运动想象算法可解读用户意图，驱动虚拟吸引子位置移动，从而使末端执行器受其牵引。我们重点聚焦于基于平面软体机器人的架构化超材料，这需要开发新型控制架构以处理其特有的非线性特性（例如控制非仿射性）。通过设定点调节任务，我们对该方法进行了初步但定量的评估。观察表明，用户在66%的步骤中能够接近设定点，且成功步骤的平均响应时间为21.5秒。我们还展示了涉及环境交互的简单现实世界任务的执行效果——若非软体机器人的柔顺特性，这些任务将极难完成。