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About this paper
Xiuchao Sui, Daiying Tian, Qi Sun, Ruirui Chen, Dongkyu Choi, Kenneth Kwok, Soujanya Poria
This research provides an empirical evaluation of three Foundation Model integration strategies (end-to-end Vision-Language-Action models, modular Vision-Language Model pipelines, Multimodal Large Language Model agents) in embodied robotics. The study quantifies their performance, generalization, data efficiency, and robustness through detailed instruction grounding and object manipulation case studies, releasing a new benchmark dataset and a claw-machine robot system for community use.
Key insights
- Proprietary MLLMs exhibit strong visual commonsense and relational reasoning capabilities for complex instruction grounding, outperforming specialist VLMs and most open-source MLLMs.
- Generalist VLA models demonstrate data inefficiency and slower adaptation to new tasks with limited, distributionally shifted data compared to more compact, task-specific models.
- Current VLA models show fragility and substantial performance degradation when confronted with novel distractor objects or significant distribution shifts in manipulation environments.
Summary by alphaXiv.
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