25 citations · 28 across the 7 of their papers we have counts for
8 papers
From Global to Local: Hierarchical Probabilistic Verification for Reachability Learning
Ebonye Smith, Sampada Deglurkar, Jingqi Li +2
Hamilton-Jacobi (HJ) reachability provides formal safety guarantees for nonlinear systems. However, it becomes computationally intractable in high-dimensional settings, motivating…
Active Calibration of Reachable Sets Using Approximate Pick-to-Learn
Sampada Deglurkar, Ebonye Smith, Jingqi Li +1
Reachability computations that rely on approximate or learned models require calibration in order to uphold confidence about their guarantees. Calibration generally involves sampli…
System-Level Analysis of Module Uncertainty Quantification in the Autonomy Pipeline
Sampada Deglurkar, Haotian Shen, Anish Muthali +5
Modern autonomous systems with machine learning components often use uncertainty quantification to help produce assurances about system operation. However, there is a lack of conse…
Multi-Agent Reachability Calibration with Conformal Prediction
Anish Muthali, Haotian Shen, Sampada Deglurkar +4
We investigate methods to provide safety assurances for autonomous agents that incorporate predictions of other, uncontrolled agents' behavior into their own trajectory planning. G…
Compositional Learning-based Planning for Vision POMDPs
Sampada Deglurkar, Michael H. Lim, Johnathan Tucker +3
The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty. However, most…
Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections
Andreea Bobu, Andrea Bajcsy, Jaime F. Fisac +2
Human input has enabled autonomous systems to improve their capabilities and achieve complex behaviors that are otherwise challenging to generate automatically. Recent work focuses…
A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance
Andrea Bajcsy, Sylvia L. Herbert, David Fridovich-Keil +4
Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents,…
Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning
David Fridovich-Keil, Sylvia L. Herbert, Jaime F. Fisac +2
Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few i…