高质量具身智能机器人数据集

该数据集记录具身智能机器人在复杂环境中执行长程任务的全过程交互数据，其内容可以覆盖多样化的场景、任务类型和机器人平台，为具身智能大模型的开发提供数据基础。基于该数据集，可支持训练具备良好泛化能力的端到端具身智能大模型，模型能够从数据集中学习通用的环境理解、任务规划与运动控制能力，实现跨场景、跨任务、跨平台的迁移适应。由此训练出的模型具有通用操作能力，能够适应不同环境并可靠执行指定任务。This dataset records full-process interactive data of embodied intelligent robots performing long-horizon tasks in complex environments. Its content covers diverse scenarios, task types and robot platforms, serving as a data foundation for the development of embodied large language models (LLMs). Based on this dataset, it can support the training of end-to-end embodied LLMs with strong generalization capabilities. These models can learn general environmental understanding, task planning and motion control abilities from the dataset, and achieve cross-scenario, cross-task and cross-platform transfer adaptation. Models trained on this dataset possess general operational capabilities, can adapt to different environments and reliably execute assigned tasks.

搜集汇总

背景与挑战

背景概述

该数据集记录了具身智能机器人在复杂环境中执行长程任务的交互数据，涵盖多样化场景、任务类型和机器人平台，为开发具身智能大模型提供数据基础。它支持训练端到端模型，使模型学习通用的环境理解、任务规划与运动控制能力，实现跨场景、跨任务、跨平台的迁移适应，从而提升机器人的通用操作能力和任务执行可靠性。