Spotter: Let the Embodied Model Lead, and the VLM Reflect for It
Overview
This research introduces Spotter, a framework that reverses the typical role of Vision-Language Models (VLMs) in embodied AI tasks. Instead of having the VLM plan every step, the embodied model leads execution while a parallel VLM monitors for errors and intervenes only when failures are detected. The authors report significant improvements on benchmarks like RoboCasa and RoboTwin 2.0 using Qwen and GPT as the VLM components, with minimal latency overhead compared to standalone embodied models.
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Reported quantity · additional time per episode: 13 other · Basis not reported
“takes only 13 to 16 s longer than with the embodied model alone and about 70% less time than with a VLM-led baseline using the same model. Our code is available at https://github.com/zqc3117/Spotter.”
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“takes only 13 to 16 s longer than with the embodied model alone and about 70% less time than with a VLM-led baseline using the same model. Our code is available at https://github.com/zqc3117/Spotter.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSpotter: Let the Embodied Model Lead, and the VLM Reflect for It
This research introduces Spotter, a framework that reverses the typical role of Vision-Language Models (VLMs) in embodied AI tasks. Instead of having the VLM plan every step, the embodied model leads execution while a parallel VLM monitors for errors and intervenes only when failures are detected. The authors report significant improvements on benchmarks like RoboCasa and RoboTwin 2.0 using Qwen and GPT as the VLM components, with minimal latency overhead compared to standalone embodied models.
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