机器人用力反馈剥下钩环带，自适应复杂环境且能耗低。
Optimal Robotic Velcro Peeling with Force Feedback
- 基于静力学假设建模，用位置与受力信息推断状态。
- 在未知环境中实现100%成功率，能耗仅比最优解高80%以内。
- 适合需要精准力控的工业抓取场景，如自动化装配。
我们研究使用机械臂从任意未知表面剥离钩环带的问题。机器人仅能获取末端执行器位置和力反馈，环境部分可观测且传感器信息不完整，带来挑战。首先基于准静态动力学假设建立简化的状态与动作模型。在完全可观测情形下，求得最小化总能量成本的闭式最优解。针对部分可观测情况，设计仅依赖力与位置反馈的状态估计算法，并提出一种启发式控制器，平衡探索与利用以高效剥离钩环带。在具有复杂几何不确定性和传感器噪声的环境中评估，方法达成100%成功率，能耗较完全可观测最优解增加不足80%，显著优于基线方法。
原文摘要 · Abstract (English)
We study the problem of peeling a Velcro strap from a surface using a robotic manipulator. The surface geometry is arbitrary and unknown. The robot has access to only the force feedback and its end-effector position. This problem is challenging due to the partial observability of the environment and the incompleteness of the sensor feedback. To solve it, we first model the system with simple analytic state and action models based on quasi-static dynamics assumptions. We then study the fully-observable case where the state of both the Velcro and the robot are given. For this case, we obtain the optimal solution in closed-form which minimizes the total energy cost. Next, for the partially-observable case, we design a state estimator which estimates the underlying state using only force and position feedback. Then, we present a heuristics-based controller that balances exploratory and exploitative behaviors in order to peel the velcro efficiently. Finally, we evaluate our proposed method in environments with complex geometric uncertainties and sensor noises, achieving 100% success rate with less than 80% increase in energy cost compared to the optimal solution when the environment is fully-observable, outperforming the baselines by a large margin.
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