Title: BEAR: Benchmarking and Enhancing Multimodal Language Models for Atomic Embodied Capabilities
Primary area: datasets and benchmarks
Keywords: MLLM benchmark and evaluation, Multi-modal Large Language Models, Embodied AI, Vision Language Models, Embodied agent

Abstract: 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's perception, 3D understanding, and planning capabilities. It substantially enhances MLLMs’ 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 enhancing MLLM's embodied capabilities can benefit embodied tasks in simulation environment.

TL;DR: a multimodal language model benchmark on embodied capabilities and a multimodal conservable embodied agent