# Safety-Critical Manipulation for Collision-Free Food Preparation Source: [Safety-Critical Manipulation for Collision-Free Food Preparation]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:11:46.889Z Authors: Andrew Singletary, William Guffey, Tamas G. Molnar, Ryan Sinnet, Aaron D. Ames Published: 2022-05-02T17:01:28.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Robot Manipulation on Robomimic (Specific Success Rates)](): Robot Manipulation; RoboMimic; Can Success Rate: 0. ## Original sources and implementation [Original paper]() [PDF]()