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.

翻译：基于学习的控制器（如扩散策略和视觉语言动作模型）通常生成低频或不连续的机器人状态变化。实现平滑的参考跟踪需要一种底层控制器，将高层目标指令转换为关节力矩，从而在接触交互过程中实现顺应行为。本文提出CRISP，一种为ROS2控制标准设计的轻量级C++实现，提供合规笛卡尔空间与关节空间控制器，旨在与高层基于学习的策略及遥操作系统实现无缝集成。该控制器兼容任何提供关节力矩接口的机械臂。通过我们的Python和Gymnasium接口，CRISP为硬件与仿真环境中的数据记录以及高层基于学习策略的无缝部署提供了统一流程，有助于快速实验验证。本系统已在Franka Robotics FR3实体硬件以及Kuka IIWA14和Kinova Gen3仿真环境中完成验证。该实现专为快速集成、灵活部署和实时性能而设计，为数据收集与策略执行提供统一流程，降低了在兼容ROS2的机械臂上应用基于学习方法的门槛。详细文档可在项目网站https://utiasDSL.github.io/crisp_controllers获取。