SnareNet introduces a repair layer that navigates the range space of constraints plus adaptive relaxation training to enforce hard non-convex constraints on neural network outputs more reliably than prior methods.
A collision cone approach for control barrier functions.arXiv preprint arXiv:2403.07043, 2024
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A sampling-based algorithm builds feasible spatiotemporal tubes for differential-drive robots to satisfy temporal reach-avoid-stay specifications, paired with a closed-form robust controller.
citing papers explorer
-
SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints
SnareNet introduces a repair layer that navigates the range space of constraints plus adaptive relaxation training to enforce hard non-convex constraints on neural network outputs more reliably than prior methods.
-
Temporal Reach-Avoid-Stay Control for Differential Drive Systems via Spatiotemporal Tubes
A sampling-based algorithm builds feasible spatiotemporal tubes for differential-drive robots to satisfy temporal reach-avoid-stay specifications, paired with a closed-form robust controller.