标题： RoboBrain 2.0 技术报告 显示英文标题

标题： RoboBrain 2.0 Technical Report

摘要： 我们介绍了RoboBrain 2.0，这是我们最新一代的具身视觉-语言基础模型，旨在统一物理环境中的复杂具身任务的感知、推理和规划。 它有两种变体：轻量级的7B模型和全规模的32B模型，具有异构架构，包括一个视觉编码器和一个语言模型。 尽管体积小巧，RoboBrain 2.0在广泛的具身推理任务中表现出色。 在空间和时间基准测试中，32B变体取得了领先结果，超过了之前开源和专有模型。 特别是，它支持关键的现实世界具身AI能力，包括空间理解（例如，功能预测、空间指代、轨迹预测）和时间决策（例如，闭环交互、多智能体长期规划和场景图更新）。 本报告详细介绍了模型架构、数据构建、多阶段训练策略、基础设施和实际应用。 我们希望RoboBrain 2.0能推动具身AI研究，并作为构建通用具身代理的实际步骤。 代码、检查点和基准可在https://superrobobrain.github.io获取。 显示英文摘要

摘要： We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial and temporal benchmarks, the 32B variant achieves leading results, surpassing prior open-source and proprietary models. In particular, it supports key real-world embodied AI capabilities, including spatial understanding (e.g., affordance prediction, spatial referring, trajectory forecasting) and temporal decision-making (e.g., closed-loop interaction, multi-agent long-horizon planning, and scene graph updating). This report details the model architecture, data construction, multi-stage training strategies, infrastructure and practical applications. We hope RoboBrain 2.0 advances embodied AI research and serves as a practical step toward building generalist embodied agents. The code, checkpoint and benchmark are available at https://superrobobrain.github.io.