arXiv 2509.170572026-05-25cs.RO

RoboManipBaselines：面向真实与仿真环境的机器人操作模仿学习统一框架

RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments

Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)（日本国家先进工业科学技术研究院人工智能研究中心）；CNRS-AIST JRL (Joint Robotics Laboratory), IRL（法国国家科学研究中心与日本AIST联合机器人实验室）；Institute for AI and Robotics, Future Robotics Organization, Waseda University（早稻田大学未来机器人组织人工智能与机器人研究所）；AI Robot Association (AIRoA)（人工智能机器人协会）

Masaki Murooka, Tomohiro Motoda, Ryoichi Nakajo, Hanbit Oh, Koshi Makihara, Keisuke Shirai, Tetsuya Ogata, Yukiyasu Domae

备注：: Added a Limitations section in response to comments from reviewers
发表信息：: IEEE Access 2026
DOI：: 10.1109/ACCESS.2026.3691054

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AI总结：

提出RoboManipBaselines开源框架，统一支持仿真和真实环境下的机器人操作模仿学习全流程，包括数据收集、策略训练和部署，并通过基准测试和研究应用验证其有效性。

摘要：

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，包括数据收集、策略训练和部署，覆盖仿真和真实环境。其设计强调通过一致的工作流程实现集成，跨不同环境和机器人平台的通用性，通过易于添加新机器人、任务和策略的可扩展性，以及通过使用公开数据集进行评估的可重复性。RoboManipBaselines系统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和真实机器人环境，以及多模态传感器和多种策略模型。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、与触觉模型的集成、交互式机器人系统、3D感知评估和硬件扩展。这些结果表明，RoboManipBaselines为利用模仿学习推进机器人操作的研究和实验验证提供了有用的基础。https://isri-aist.github.io/RoboManipBaselines-ProjectPage

英文摘要：

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

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。