Optimal Robotic Velcro Peeling with Force Feedback

TL;DR

This paper develops an energy-efficient, force-feedback-based robotic peeling method for arbitrary surfaces, combining optimal control, state estimation, and heuristics to handle partial observability and environmental uncertainties.

Contribution

It introduces a novel approach that integrates analytic modeling, state estimation, and heuristics for robotic Velcro peeling under partial observability.

Findings

Achieves 100% success rate in complex environments

Maintains less than 80% energy increase compared to optimal

Outperforms baseline methods significantly

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.