8 papers
Learning-Based Measurement-Robust Control Barrier Functions for Obstacle Avoidance under State Estimation Error
Nicholas Rober, Yixuan Jia, Jonathan P. How
Safety filters are an effective tool for enforcing constraints in safety-critical systems, but most existing methods assume perfect state information, which is rarely available in…
Beyond Bayesian Nash: Learning Minimax-Regret Equilibria for Adversarial Team Games under Asymmetric Information
Naman Aggarwal, Jonathan P. How
Adversarial team games (ATGs) with asymmetric information, such as adversarial path-finding, goal search, and reachability games on graphs, require strategies that are robust to hi…
PRISM: Efficient and Locally Optimal Probabilistic Planning with Reachability Guarantees
Alex Rose, Christopher Jewison, Jonathan P. How
Belief-space planning under motion uncertainty and state and control constraints remains a fundamental challenge, largely due to the difficulty of establishing reachability guarant…
Robust Sampling-Based Covariance Steering for Aerocapture Guidance
Alex Rose, Christopher Jewison, Jonathan P. How
Aerocapture is a maneuver where a spacecraft dives through the atmosphere of a planet or moon to reduce its velocity and prepare for orbital insertion. Aerocapture allows for highe…
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…
GUARDIAN: Safety Filtering for Systems with Perception Models Subject to Adversarial Attacks
Nicholas Rober, Alex Rose, Jonathan P. How
Safety filtering is an effective method for enforcing constraints in safety-critical systems, but existing methods typically assume perfect state information. This limitation is es…