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About this paper
Xueyang Zhou, Zijia Wang, Qianjiang Li, Yibo Hu, Guiyao Tie, Li Wan, Yidan Liu, Pan Zhou and 2 more
Researchers from Huazhong University of Science and Technology and collaborators introduce a framework for embodied AI that externalizes heterogeneous capabilities into independently optimized tools. The work establishes the Embodied Tool Protocol (ETP) and a benchmark, EmbodiedToolBench, showing consistent performance gains across various tasks, with average improvements of up to 36% in navigation, while revealing persistent challenges in tool-need recognition and selection.
Key insights
- Externalizing capabilities through tools consistently enhances embodied task performance and improves the competitiveness of open-source models, enabling them to match or exceed the performance of stronger monolithic models.
- Models exhibit challenges in 'tool competence,' particularly regarding reliable tool-need recognition, precise tool selection from similar options, and effective interpretation of tool outputs.
- Perception and cognition capabilities benefit most significantly from tool augmentation, while improvements in low-level execution and fine-grained manipulation are comparatively smaller.
Summary by alphaXiv.
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