软机器人用刷毛清洁顽固污渍，99.7%去除率。
SCCRUB: Surface Cleaning Compliant Robot Utilizing Bristles
- 用神经网络学习软臂逆运动学与弹性，实现开环力控
- 成功清除盘子烧焦食物、马桶座黏性果酱，平均去污99.7%
- 安全适配人机共存环境，适合家庭清洁场景
擦洗表面是体力消耗大且耗时的任务。清除附着污染物需通过压力和扭矩或高横向力产生显著摩擦。刚性机械臂虽能施加这些力，但通常因安全风险仅限于无人环境。相比之下，软体机械臂可安全与人协作并适应环境不确定性，但通常难以传递持续扭矩或横向力以完成擦洗。本文展示了一种利用刷毛的软体机械臂，通过扭矩与压力清除附着残留物，该任务传统上对软体机器人极具挑战。我们训练神经网络以学习机械臂的逆运动学与弹性特性，实现开环力控与位置控制。基于该学习模型，机器人成功清除盘中烧焦食物及马桶座黏性果酱，平均去除率达99.7%。本研究证明，具备持续扭矩输出能力的软体机器人可安全高效地清洁复杂污染物。
原文摘要 · Abstract (English)
Scrubbing surfaces is a physically demanding and time-intensive task. Removing adhered contamination requires substantial friction generated through pressure and torque or high lateral forces. Rigid robotic manipulators, while capable of exerting these forces, are usually confined to structured environments isolated from humans due to safety risks. In contrast, soft robot arms can safely work around humans and adapt to environmental uncertainty, but typically struggle to transmit the continuous torques or lateral forces necessary for scrubbing. Here, we demonstrate a soft robotic arm scrubbing adhered residues using torque and pressure, a task traditionally challenging for soft robots. We train a neural network to learn the arm's inverse kinematics and elasticity, which enables open-loop force and position control. Using this learned model, the robot successfully scrubbed burnt food residue from a plate and sticky fruit preserve from a toilet seat, removing an average of 99.7% of contamination. This work demonstrates how soft robots, capable of exerting continuous torque, can effectively and safely scrub challenging contamination from surfaces.
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