一个统一框架，让机器人在仿真和真实环境中都能高效学习操作技能。
RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
- 统一接口支持仿真与真实环境的数据采集、训练和测试
- 兼容多种机器人、传感器和策略模型，可扩展性强
- 适合研究者快速验证新方法，尤其适合具身智能与仿生操控方向
我们提出 RoboManipBaselines，一个开源软件框架，用于机器人操作中的模仿学习研究。该框架覆盖从数据收集、策略训练到推理的完整流程，支持仿真与真实环境。设计注重工作流一致性、跨平台通用性、可扩展性及可复现性，通过公开数据集进行评估。框架系统实现模仿学习核心组件：环境、数据集与策略模型。通过统一接口，支持多种模拟器、真实机器人环境、多模态传感器和多样策略模型。我们还在仿真与真实场景中开展基准评测，并展示多个应用，包括数据增强、触觉模型融合、交互式机器人系统、3D感知评估与硬件扩展。结果表明，RoboManipBaselines 为模仿学习在机器人操作中的研究与实验验证提供了坚实基础。
原文摘要 · Abstract (English)
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
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