BEAR: Benchmarking and Enhancing Multimodal Language Models for Atomic
  Embodied Capabilities
Embodied capabilities refer to a suite of fundamental abilities for an agent
to perceive, comprehend, and interact with the physical world. While multimodal
large language models (MLLMs) show promise as embodied agents, a thorough and
systematic evaluation of their embodied capabilities remains underexplored, as
existing benchmarks primarily focus on specific domains such as planning or
spatial understanding. To bridge this gap, we introduce BEAR, a comprehensive
and fine-grained benchmark that evaluates MLLMs on atomic embodied
capabilities. BEAR comprises 4,469 interleaved image-video-text entries across
14 domains in 6 categories, including tasks from low-level pointing, trajectory
understanding, spatial reasoning, to high-level planning. Extensive evaluation
results of 20 representative MLLMs reveal their persistent limitations across
all domains of embodied capabilities. To tackle the shortfall, we propose
BEAR-Agent, a multimodal conversable agent that integrates pretrained vision
models to strengthen MLLM perception, 3D understanding, and planning
capabilities. It substantially enhances MLLM performance across diverse
embodied capabilities on BEAR, yielding a 9.12% absolute gain and a relative
improvement of 17.5% on GPT-5. Furthermore, our experiments indicate that
improving MLLM embodied capabilities can benefit embodied tasks in simulated
environments. Project website: https://bear-official66.github.io/
68ecdddfb0aa732b88f31f4f
Yu Qi
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yu
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Haibo Zhao
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2025-10-14T07:31:53.874Z
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haibozhao
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haibozzz
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Ziyu Guo
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Siyuan Ma
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Siyuan Ma
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Ziyan Chen
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Ziyan Chen
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Yaokun Han
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YAOKUN HAN
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PersioHAN
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Renrui Zhang
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Zitiantao Lin
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Shiji Xin
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Yijian Huang
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2025-10-14T07:31:43.071Z
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Yijian Huang
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Kai Cheng
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Kai Cheng
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kaichengpku
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Peiheng Wang
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Jiazheng Liu
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Jiayi Zhang
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Eleanor Zhang
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EleanorZzz
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Yizhe Zhu
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Wenqing Wang
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Yiran Qin
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Xupeng Zhu
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Haojie Huang
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Lawson L. S. Wong
BEAR: Benchmarking and Enhancing Multimodal Language Models for Atomic
  Embodied Capabilities
Embodied capabilities refer to a suite of fundamental abilities for an agent
to perceive, comprehend, and interact with the physical world. While multimodal
large language models (MLLMs) show promise as embodied agents, a thorough and
systematic evaluation of their embodied capabilities remains underexplored, as
existing benchmarks primarily focus on specific domains such as planning or
spatial understanding. To bridge this gap, we introduce BEAR, a comprehensive
and fine-grained benchmark that evaluates MLLMs on atomic embodied
capabilities. BEAR comprises 4,469 interleaved image-video-text entries across
14 domains in 6 categories, including tasks from low-level pointing, trajectory
understanding, spatial reasoning, to high-level planning. Extensive evaluation
results of 20 representative MLLMs reveal their persistent limitations across
all domains of embodied capabilities. To tackle the shortfall, we propose
BEAR-Agent, a multimodal conversable agent that integrates pretrained vision
models to strengthen MLLM perception, 3D understanding, and planning
capabilities. It substantially enhances MLLM performance across diverse
embodied capabilities on BEAR, yielding a 9.12% absolute gain and a relative
improvement of 17.5% on GPT-5. Furthermore, our experiments indicate that
improving MLLM embodied capabilities can benefit embodied tasks in simulated
environments. Project website: https://bear-official66.github.io/
BEAR is a comprehensive benchmark evaluating multimodal large language models' embodied capabilities, and BEAR-Agent enhances these models by integrating pretrained vision models, improving performance across various tasks.
multimodal large language models embodied capabilities BEAR image-video-text entries trajectory understanding spatial reasoning planning capabilities BEAR-Agent pretrained vision models 3D understanding