3 citations · 3 across the 2 of their papers we have counts for
7 papers · 1 filter
A Forward Reachability Perspective on Control Barrier Functions and Discount Factors in Reachability Analysis
Jason J. Choi, Donggun Lee, Boyang Li +4
Control invariant sets are crucial for various methods that aim to design safe control policies for systems whose state constraints must be satisfied over an indefinite time horizo…
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…
Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underactuated Systems Navigating on Uncertain Ocean Currents
Matthias Killer, Marius Wiggert, Hanna Krasowski +3
Seaweed biomass presents a substantial opportunity for climate mitigation, yet to realize its potential, farming must be expanded to the vast open oceans. However, in the open ocea…
Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games
Jingqi Li, Chih-Yuan Chiu, Lasse Peters +6
Decision-making in multi-player games can be extremely challenging, particularly under uncertainty. In this work, we propose a new sample-based approximation to a class of stochast…
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning
Jingqi Li, Donggun Lee, Somayeh Sojoudi +1
In this paper, we consider the infinite-horizon reach-avoid zero-sum game problem, where the goal is to find a set in the state space, referred to as the reach-avoid set, such that…
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions
Fernando Castañeda, Jason J. Choi, Wonsuhk Jung +3
Learning-based control has recently shown great efficacy in performing complex tasks for various applications. However, to deploy it in real systems, it is of vital importance to g…