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Found 4 papers, 2 papers with code
Multi-Agent Reachability Calibration with Conformal Prediction
We investigate methods to provide safety assurances for autonomous agents that incorporate predictions of other, uncontrolled agents' behavior into their own trajectory planning.
Compositional Learning-based Planning for Vision POMDPs
The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty.
Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections
Recent work focuses on how robots can use such input - like demonstrations or corrections - to learn intended objectives.
Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning
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 if any safety guarantees, or able to give stronger guarantees but at high computational cost.
Systems and Control Computer Science and Game Theory
