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20202025
most citedGuaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions

9 citations · 11 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.RO2025

Risk-Aware Safety Filters with Poisson Safety Functions and Laplace Guidance Fields

Gilbert Bahati, Ryan M. Bena, Meg Wilkinson +3

Robotic systems navigating in real-world settings require a semantic understanding of their environment to properly determine safe actions. This work aims to develop the mathematic…

cs.RO2025

Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC

Ryan M. Bena, Gilbert Bahati, Blake Werner +3

Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular,…

cs.RO2025

Learning Safe Control via On-the-Fly Bandit Exploration

Alexandre Capone, Ryan Cosner, Aaaron Ames +1

Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typica…

cs.RO20222 cited

Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision

Ryan K. Cosner, Ivan D. Jimenez Rodriguez, Tamas G. Molnar +4

With the increasing prevalence of complex vision-based sensing methods for use in obstacle identification and state estimation, characterizing environment-dependent measurement err…

cs.RO2021

Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety

Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai +2

This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only vali…