BEAR: Benchmarking and Enhancing Multimodal Language Models for Atomic Embodied Capabilities
This addresses the problem of evaluating and enhancing embodied AI agents for researchers and developers, though it is incremental as it builds on existing MLLM and benchmark work.
The authors tackled the lack of systematic evaluation of multimodal large language models (MLLMs) for embodied capabilities by introducing BEAR, a comprehensive benchmark, and found persistent limitations across 20 MLLMs, then proposed BEAR-Agent, which improved performance by 9.12% absolute and 17.5% relative on GPT-5.
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/