We introduce RoboNinja, a learning-based cutting system for multi-material objects (i.e., soft objects with rigid cores such as avocados or mangos). In contrast to prior works using open-loop cutting actions to cut through single-material objects (e.g., slicing a cucumber), RoboNinja aims to remove the soft part of an object while preserving the rigid core, thereby maximizing the yield. To achieve this, our system closes the perception-action loop by utilizing an interactive state estimator and an adaptive cutting policy. The system first employs sparse collision information to iteratively estimate the position and geometry of an object's core and then generates closed-loop cutting actions based on the estimated state and a tolerance value. The "adaptiveness" of the policy is achieved through the tolerance value, which modulates the policy's conservativeness when encountering collisions, maintaining an adaptive safety distance from the estimated core. Learning such cutting skills directly on a real-world robot is challenging. Yet, existing simulators are limited in simulating multi-material objects or computing the energy consumption during the cutting process. To address this issue, we develop a differentiable cutting simulator that supports multi-material coupling and allows for the generation of optimized trajectories as demonstrations for policy learning. Furthermore, by using a low-cost force sensor to capture collision feedback, we were able to successfully deploy the learned model in real-world scenarios, including objects with diverse core geometries and soft materials.

翻译：我们提出了罗布忍者（RoboNinja），一个基于学习的多材质物体切割系统（即含有坚硬内核的柔软物体，如牛油果或芒果）。与以往采用开环切割动作处理单材质物体（例如切黄瓜）的研究不同，罗布忍者的目标是在保留坚硬内核的同时切除物体的柔软部分，从而最大化产量。为实现这一目标，我们的系统通过交互式状态估计器和自适应切割策略，构建了感知-动作闭环。系统首先利用稀疏碰撞信息迭代估计物体内核的位置与几何形状，随后根据估计状态和容差阈值生成闭环切割动作。策略的"自适应性"通过容差阈值实现——该参数在遇到碰撞时调节策略的保守程度，以维持与估计内核间的自适应安全距离。直接在真实机器人上学习此类切割技能极具挑战性。然而，现有仿真器在模拟多材质物体或计算切割过程能耗方面存在局限。为解决此问题，我们开发了支持多材质耦合的可微分切割仿真器，能生成优化轨迹作为策略学习的示范。此外，通过使用低成本力传感器捕捉碰撞反馈，我们成功将学习后的模型部署于真实场景中，涵盖了具有不同内核几何形状和柔软材质的物体。