A Perception-Manipulation Robotics System for Food Cutting

2026-07-05

AI Summary

This work addresses the challenge of dynamically selecting cutting tools and strategies for robotic cooking systems when handling food items with diverse physical properties. The authors propose an integrated perception-action framework for adaptive food cutting, which leverages force feedback from preliminary incision trials to autonomously select appropriate cutting tools and employs reinforcement learning to optimize cutting policies in real time. By uniquely combining force-guided tool selection with reinforcement learning–based control, the system achieves 100% tool selection accuracy and high-precision fully automated cutting on previously unseen ingredients. The overall performance matches that of human operators while effectively balancing cutting speed and energy consumption.

Abstract

In the development of cooking robots, mastering the task of cutting is crucial. A significant challenge lies in the diverse properties of food, which necessitate distinct cutting policies and even different knives for optimal processing. This paper presents a perception-manipulation framework for food-cutting tasks. Our system features a knife selection module that utilizes force data from a preliminary fixed trial cut to select the appropriate knife for the given food. This is followed by an adaptive cutting phase using reinforcement learning (RL) to balance cutting speed and energy efficiency. In our experiments, the knife selection module achieved 100% successful rate on unseen food, and we compared the performances of fixed policy, RL policy, with human operators. Our method not only achieves high performance but also demonstrates comparable results to those of human participants.

Problem

• food cutting
• cooking robots
• knife selection
• adaptive manipulation
• perception-manipulation

Innovation

• knife selection
• reinforcement learning
• food cutting
• perception-manipulation framework
• adaptive cutting