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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.

Part of the event: AlphaAdj: Using VLM Semantics to Adjust CBF Conservativeness

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