Safety-Critical Manipulation for Collision-Free Food Preparation

TL;DR

This paper introduces a novel, real-time control framework using Control Barrier Functions to ensure collision-free, safe manipulation in dynamic food preparation environments, significantly improving robustness and computational efficiency.

Contribution

The work presents a new CBF-based method for dynamically re-planning robotic trajectories with safety guarantees in complex, changing environments, specifically applied to food preparation robots.

Findings

Substantial reduction in computation time for trajectory re-planning

Enhanced robustness of collision avoidance in dynamic settings

Successful validation on a real-world robotic cooking system

Abstract

Recent advances allow for the automation of food preparation in high-throughput environments, yet the successful deployment of these robots requires the planning and execution of quick, robust, and ultimately collision-free behaviors. In this work, we showcase a novel framework for modifying previously generated trajectories of robotic manipulators in highly detailed and dynamic collision environments using Control Barrier Functions (CBFs). This method dynamically re-plans previously validated behaviors in the presence of changing environments -- and does so in a computationally efficient manner. Moreover, the approach provides rigorous safety guarantees of the resulting trajectories, factoring in the true underlying dynamics of the manipulator. This methodology is extensively validated on a full-scale robotic manipulator in a real-world cooking environment, and has resulted in substantial improvements in computation time and robustness over re-planning.