RoboNinja: Learning an Adaptive Cutting Policy for Multi-Material Objects
RoboNinja: Learning an Adaptive Cutting Policy for Multi-Material Objects
复制标题
DOI：
10.48550/arxiv.2302.11553
复制
发表时间：
2023-02
期刊：
影响因子：
--
通讯作者：
Zhenjia Xu;Zhou Xian;Xingyu Lin;Cheng Chi;Zhiao Huang;Chuang Gan;Shuran Song
中科院分区：
文献类型：
--
作者：
Zhenjia Xu;Zhou Xian;Xingyu Lin;Cheng Chi;Zhiao Huang;Chuang Gan;Shuran Song
我们介绍RoboNinja，一个基于学习的多材料物体切割系统（即，具有刚性核的柔软物体，如鳄梨或芒果）。与使用开环切割动作来切穿单一材料对象（例如，RoboNinja的目标是去除物体的柔软部分，同时保留刚性核心，从而最大限度地提高产量。为了实现这一点，我们的系统关闭的感知行动循环，利用交互式状态估计器和自适应切割政策。该系统首先采用稀疏碰撞信息迭代估计的位置和几何形状的对象的核心，然后生成闭环切割动作的基础上估计的状态和公差值。该策略的“适应性“是通过公差值来实现的，公差值在遇到碰撞时调节策略的保守性，与估计的核心保持自适应的安全距离。直接在真实世界的机器人上学习这种切割技能是具有挑战性的。然而，现有的模拟器在模拟多材料对象或计算切割过程中的能量消耗方面受到限制。为了解决这个问题，我们开发了一个微分切削模拟器，支持多材料耦合，并允许生成优化的轨迹作为政策学习的示范。此外，通过使用低成本的力传感器来捕获碰撞反馈，我们能够成功地将学习的模型部署在现实世界的场景中，包括具有不同核心几何形状和软材料的物体。
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.