Event
AlphaAdj: Using VLM Semantics to Adjust CBF Conservativeness
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Jeffrey Chen and Rohan Chandra propose AlphaAdj, a vision-to-control robot navigation framework: it uses first-person RGB observations and a vision-language model (VLM) to adjust the conservativeness of a control barrier function (CBF) according to visual semantic context. The VLM outputs a scalar "contextual caution" signal that dynamically adjusts the CBF parameter alpha, and geometry- and speed-related upper bounds constrain how aggressive it can become. To cope with VLM latency, inference and the control loop run asynchronously, and the semantic signal is taken directly from token-level output probabilities rather than from full autoregressive text generation. The authors report that the system maintains a 0.0167-second control period, with a mean VLM round-trip latency of 0.158 seconds.
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- AlphaAdj: A Robot Navigation Framework That Adaptively Tunes Control Barrier Functions with Vision-Language Models
The authors propose AlphaAdj, a vision-to-control navigation framework that uses first-person RGB observations and a vision-language model (VLM) to adjust the conservativeness of control barrier functions (CBFs) according to visual semantic context. The VLM outputs a scalar contextual caution signal that dynamically modulates the CBF parameter alpha, while a geometry- and speed-dependent cap bounds how aggressive the behavior can be. To cope with VLM latency, inference and the control loop run asynchronously, and the semantic signal is taken directly from token-level output probabilities rather than from full autoregressive text generation.
arXiv cs.RO ·
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