RoboBrain 2.0 Technical Report
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.
68679569213f123a1f88b87c
BAAI RoboBrain Team
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Mingyu Cao
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Huajie Tan
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tanhuajie2001
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Yuheng Ji
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Minglan Lin
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Zhiyu Li
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Zhou Cao
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Yi Han
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Yingbo Tang
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Mengdi Zhao
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Tiejun Huang
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Shanghang Zhang
RoboBrain 2.0 Technical Report
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.
RoboBrain 2.0, a heterogeneous vision-language model, excels in embodied reasoning tasks with strong performance on spatial and temporal benchmarks, supporting capabilities like spatial understanding and temporal decision-making.
embodied vision-language foundation models heterogeneous architecture vision encoder language model spatial understanding affordance prediction spatial referring trajectory forecasting temporal decision-making closed-loop interaction multi-agent long-horizon planning scene graph updating multi-stage training strategies