智能机器人通过感知食物特性自动选刀并自适应切菜，效果媲美人类。
A Perception-Manipulation Robotics System for Food Cutting
- 根据初切力数据自动选刀，适配不同食材
- 强化学习优化切割速度与能耗，成功率100%
- 适合厨房自动化、机器人烹饪研究者
在烹饪机器人研发中，掌握切割技能至关重要。食物性质差异大，需采用不同切割策略甚至不同刀具以实现最优处理。本文提出一种感知-操作框架用于食物切割任务。系统包含刀具选择模块，通过初步固定切削的力数据判断并选用合适刀具；随后进入自适应切割阶段，利用强化学习（RL）平衡切割速度与能量效率。实验表明，刀具选择模块在未见过的食物上达到100%成功率，并对比了固定策略、强化学习策略与人类操作员的表现。本方法不仅性能优异，且结果可媲美人类参与者。
原文摘要 · Abstract (English)
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.
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