Semantically Adaptive Control Barrier Functions with Vision-Language Models for Real-Time Robot Navigation
Overview
This research presents AlphaAdj, a navigation framework that combines egocentric RGB observations with a vision-language model to dynamically adjust control barrier function conservativeness based on semantic context. By extracting caution signals from token-level probabilities asynchronously, the system achieves real-time operation (0.0167 s control period) while allocating significantly more clearance to humans than benign objects in fixed-geometry scenarios. Evaluations on a physical Clearpath Jackal demonstrate that AlphaAdj remains collision-free in cluttered conditions where geometric baselines fail, proving that semantic visual reasoning can complement geometric safety control without entering the critical control path.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · control period: 0.0167 other · Basis not reported
“tes 0.405 m more clearance to a human than to a benign object, while geometry-only and non-visual adaptive CBF baselines produce no semantic differentiation. This additional behavioral reserve improves robustness to unexpected human motion, including cluttered conditions in which all evaluated geometric baselines collide while AlphaAdj remains collision-free. The system maintains a 0.0167 s control period with 0.158”
Exact source · revision 1Source owner not reported
Reported quantity · VLM round-trip latency: 0.158 other · Basis not reported
“tes 0.405 m more clearance to a human than to a benign object, while geometry-only and non-visual adaptive CBF baselines produce no semantic differentiation. This additional behavioral reserve improves robustness to unexpected human motion, including cluttered conditions in which all evaluated geometric baselines collide while AlphaAdj remains collision-free. The system maintains a 0.0167 s control period with 0.158”
Exact source · revision 1Source owner not reported
Reported quantity · additional clearance to human: 0.405 other · Basis not reported
“tes 0.405 m more clearance to a human than to a benign object, while geometry-only and non-visual adaptive CBF baselines produce no semantic differentiation. This additional behavioral reserve improves robustness to unexpected human motion, including cluttered conditions in which all evaluated geometric baselines collide while AlphaAdj remains collision-free. The system maintains a 0.0167 s control period with 0.158”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSemantically Adaptive Control Barrier Functions with Vision-Language Models for Real-Time Robot Navigation
This research presents AlphaAdj, a navigation framework that combines egocentric RGB observations with a vision-language model to dynamically adjust control barrier function conservativeness based on semantic context. By extracting caution signals from token-level probabilities asynchronously, the system achieves real-time operation (0.0167 s control period) while allocating significantly more clearance to humans than benign objects in fixed-geometry scenarios. Evaluations on a physical Clearpath Jackal demonstrate that AlphaAdj remains collision-free in cluttered conditions where geometric baselines fail, proving that semantic visual reasoning can complement geometric safety control without entering the critical control path.
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