5 papers
Steering with Contingencies: Combinatorial Stabilization and Reach-Avoid Filters
Yana Lishkova, Pio Ong, Sander Tonkens +2
In applications such as autonomous landing and navigation, it is often desirable to steer toward a target while retaining the ability to divert to at least (out of ) alterna…
Refining Almost-Safe Value Functions on the Fly
Sander Tonkens, Sosuke Kojima, Chenhao Liu +2
Control Barrier Functions (CBFs) are a powerful tool for ensuring robotic safety, but designing or learning valid CBFs for complex systems is a significant challenge. While Hamilto…
Safe Event-triggered Gaussian Process Learning for Barrier-Constrained Control
Armin Lederer, Azra BegzadiÄ, Sandra Hirche +2
While control barrier functions (CBFs) are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical lim…
Back to Base: Towards Hands-Off Learning via Safe Resets with Reach-Avoid Safety Filters
Azra BegzadiÄ, Nikhil Uday Shinde, Sander Tonkens +5
Designing controllers that accomplish tasks while guaranteeing safety constraints remains a significant challenge. We often want an agent to perform well in a nominal task, such as…
Sensor-Based Distributionally Robust Control for Safe Robot Navigation in Dynamic Environments
Kehan Long, Yinzhuang Yi, Zhirui Dai +3
We introduce a novel method for mobile robot navigation in dynamic, unknown environments, leveraging onboard sensing and distributionally robust optimization to impose probabilisti…