arXiv 2509.068192026-02-06cs.RO

CRISP -- 用于基于学习的操控策略和遥控的兼容ROS2控制器

CRISP -- Compliant ROS2 Controllers for Learning-Based Manipulation Policies and Teleoperation

Technical University of Munich（慕尼黑技术大学）；TUM School of Computation, Information and Technology（TUM计算、信息与技术学院）；Department of Computer Engineering（计算机工程系）；Learning Systems and Robotics Lab（学习系统与机器人实验室）；TUM Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑技术大学机器人与人工智能研究所（MIRMI））

Daniel San José Pro, Oliver Hausdörfer, Ralf Römer, Maximilian Dösch, Martin Schuck, Angela P. Schoellig

备注：: 5 pages, 5 figures

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AI总结：

CRISP是一种用于ROS2的轻量级控制器，支持基于学习的操控策略和遥控，提供统一的数据收集和策略执行管道，降低应用难度。

摘要：

基于学习的控制器，如扩散策略和视觉-语言动作模型，往往生成低频或断续的机器人状态变化。实现平滑的参考跟踪需要一个低层控制器，将高层目标命令转换为关节扭矩，从而在接触交互期间实现顺应行为。我们提出了CRISP，这是一个轻量级的C++实现的顺应笛卡尔和关节空间控制器，用于ROS2控制标准，旨在无缝集成到高层基于学习的策略以及遥控中。这些控制器兼容任何暴露关节扭矩接口的机械臂。通过我们的Python和Gymnasium接口，CRISP提供了一个统一的管道，用于从硬件和仿真中记录数据，并无缝部署高层基于学习的策略，从而促进快速实验。该系统已在Franka Robotics FR3硬件和Kuka IIWA14和Kinova Gen3仿真中得到验证。设计用于快速集成、灵活部署和实时性能，我们的实现提供了一个统一的数据收集和策略执行管道，降低了在ROS2兼容机械臂上应用基于学习的方法的障碍。详细的文档可在项目网站上找到 - https://utiasDSL.github.io/crisp_controllers。

英文摘要：

Learning-based controllers, such as diffusion policies and vision-language action models, often generate low-frequency or discontinuous robot state changes. Achieving smooth reference tracking requires a low-level controller that converts high-level targets commands into joint torques, enabling compliant behavior during contact interactions. We present CRISP, a lightweight C++ implementation of compliant Cartesian and joint-space controllers for the ROS2 control standard, designed for seamless integration with high-level learning-based policies as well as teleoperation. The controllers are compatible with any manipulator that exposes a joint-torque interface. Through our Python and Gymnasium interfaces, CRISP provides a unified pipeline for recording data from hardware and simulation and deploying high-level learning-based policies seamlessly, facilitating rapid experimentation. The system has been validated on hardware with the Franka Robotics FR3 and in simulation with the Kuka IIWA14 and Kinova Gen3. Designed for rapid integration, flexible deployment, and real-time performance, our implementation provides a unified pipeline for data collection and policy execution, lowering the barrier to applying learning-based methods on ROS2-compatible manipulators. Detailed documentation is available at the project website - https://utiasDSL.github.io/crisp_controllers.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。