We present RoboManipBaselines, an open-source software framework for imitation learning research in robotic manipulation. The framework supports the entire imitation learning pipeline, including data collection, policy training, and rollout, across both simulation and real-world environments. Its design emphasizes integration through a consistent workflow, generality across diverse environments and robot platforms, extensibility for easily adding new robots, tasks, and policies, and reproducibility through evaluations using publicly available datasets. RoboManipBaselines systematically implements the core components of imitation learning: environment, dataset, and policy. Through a unified interface, the framework supports multiple simulators and real robot environments, as well as multimodal sensors and a wide variety of policy models. We further present benchmark evaluations in both simulation and real-world environments and introduce several research applications, including data augmentation, integration with tactile models, interactive robotic systems, 3D sensing evaluation, and hardware extensions. These results demonstrate that RoboManipBaselines provides a useful foundation for advancing research and experimental validation in robotic manipulation using imitation learning. https://isri-aist.github.io/RoboManipBaselines-ProjectPage

翻译：我们提出RoboManipBaselines，一个用于机器人操作模仿学习研究的开源软件框架。该框架支持完整的模仿学习流程，包括数据采集、策略训练与部署测试，且同时适用于仿真和真实世界环境。其设计强调通过一致的工作流实现集成、跨多种环境与机器人平台的通用性、便于添加新机器人/任务/策略的可扩展性，以及基于公开数据集评估的可复现性。RoboManipBaselines系统性地实现了模仿学习的核心组件：环境、数据集和策略。通过统一接口，该框架支持多种仿真器与真实机器人环境，以及多模态传感器与各类策略模型。我们进一步在仿真和真实环境中进行了基准评估，并介绍了多项研究应用，包括数据增强、触觉模型集成、交互式机器人系统、三维感知评估及硬件扩展。这些结果表明，RoboManipBaselines为利用模仿学习推进机器人操作领域的研究与实验验证提供了重要基础。https://isri-aist.github.io/RoboManipBaselines-ProjectPage