1 citations · 1 across the 9 of their papers we have counts for
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Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems
Jean-Baptiste Bouvier, Kartik Nagpal, Negar Mehr
In this paper, we develop a method for learning a control policy guaranteed to satisfy an affine state constraint of high relative degree in closed loop with a black-box system. Pr…
Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds
Rohan Chandra, Haresh Karnan, Negar Mehr +2
Social robot navigation in crowded public spaces such as university campuses, restaurants, grocery stores, and hospitals, is an increasingly important area of research. One of the…
Integrating Predictive Motion Uncertainties with Distributionally Robust Risk-Aware Control for Safe Robot Navigation in Crowds
Kanghyun Ryu, Negar Mehr
Ensuring safe navigation in human-populated environments is crucial for autonomous mobile robots. Although recent advances in machine learning offer promising methods to predict hu…
POLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints
Jean-Baptiste Bouvier, Kartik Nagpal, Negar Mehr
In this paper, we seek to learn a robot policy guaranteed to satisfy state constraints. To encourage constraint satisfaction, existing RL algorithms typically rely on Constrained M…