Optimal Robotic Velcro Peeling with Force Feedback
🤖 AI Summary
This work addresses the problem of robust robotic manipulation of Velcro tape detachment on unknown geometric surfaces using only end-effector force and pose feedback—without vision. To tackle partial observability and sensor noise, we propose a quasi-static dynamic modeling framework and derive a closed-form energy-optimal solution under full observability. We further design a real-time state estimator based on the extended Kalman filter and develop a heuristic adaptive controller that balances exploration and exploitation. This constitutes the first closed-loop optimal Velcro detachment control strategy operating without visual sensing under partial observability. Experiments demonstrate 100% detachment success rate on complex curved surfaces under noisy conditions; energy consumption increases by less than 80% compared to the full-observability optimal baseline, significantly outperforming existing methods.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Peeling Velcro with robotic force feedback
Handling unknown arbitrary surface geometry
Managing partial observability and sensor limitations
Innovation
Methods, ideas, or system contributions that make the work stand out.
Model system with quasi-static dynamics assumptions
Design state estimator using force and position feedback
Heuristics-based controller balances exploration and exploitation
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