20 citations · 29 across the 17 of their papers we have counts for
6 papers · 1 filter
Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions
Ryan K. Cosner, Igor Sadalski, Jana K. Woo +2
A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances. Most existing approaches to robust control either seek to bound the…
PONG: Probabilistic Object Normals for Grasping via Analytic Bounds on Force Closure Probability
Albert H. Li, Preston Culbertson, Aaron D. Ames
Classical approaches to grasp planning are deterministic, requiring perfect knowledge of an object's pose and geometry. In response, data-driven approaches have emerged that plan g…
Input-to-State Stability in Probability
Preston Culbertson, Ryan K. Cosner, Maegan Tucker +1
Input-to-State Stability (ISS) is fundamental in mathematically quantifying how stability degrades in the presence of bounded disturbances. If a system is ISS, its trajectories wil…
Robust Safety under Stochastic Uncertainty with Discrete-Time Control Barrier Functions
Ryan K. Cosner, Preston Culbertson, Andrew J. Taylor +1
Robots deployed in unstructured, real-world environments operate under considerable uncertainty due to imperfect state estimates, model error, and disturbances. Given this real-wor…
FRoGGeR: Fast Robust Grasp Generation via the Min-Weight Metric
Albert H. Li, Preston Culbertson, Joel W. Burdick +1
Many approaches to grasp synthesis optimize analytic quality metrics that measure grasp robustness based on finger placements and local surface geometry. However, generating feasib…
CATNIPS: Collision Avoidance Through Neural Implicit Probabilistic Scenes
Timothy Chen, Preston Culbertson, Mac Schwager
We introduce a transformation of a Neural Radiance Field (NeRF) to an equivalent Poisson Point Process (PPP). This PPP transformation allows for rigorous quantification of uncertai…