10 papers
Temporal logics and formal synthesis for robot planning and control
Jana Tumova, Joris Verhagen, Matti Vahs
As robots move from controlled environments into real-world settings, it becomes increasingly crucial to ensure that they perform as expected. A key step toward that goal is a rigo…
Safety-critical Control Under Partial Observability: Reach-Avoid POMDP meets Belief Space Control
Matti Vahs, Joris Verhagen, Jana Tumova
Partially Observable Markov Decision Processes (POMDPs) provide a principled framework for robot decision-making under uncertainty. Solving reach-avoid POMDPs, however, requires co…
Learning to Localize Reference Trajectories in Image-Space for Visual Navigation
Finn Lukas Busch, Matti Vahs, Quantao Yang +4
We present LoTIS, a model for visual navigation that provides robot-agnostic image-space guidance by localizing a reference RGB trajectory in the robot's current view, without requ…
Parameter-Robust MPPI for Safe Online Learning of Unknown Parameters
Matti Vahs, Jaeyoun Choi, Niklas Schmid +2
Robots deployed in dynamic environments must remain safe even when key physical parameters are uncertain or change over time. We propose Parameter-Robust Model Predictive Path Inte…
Risk-Aware Robot Control in Dynamic Environments Using Belief Control Barrier Functions
Shaohang Han, Matti Vahs, Jana Tumova
Ensuring safety for autonomous robots operating in dynamic environments can be challenging due to factors such as unmodeled dynamics, noisy sensor measurements, and partial observa…
Finding Control Invariant Sets via Lipschitz Constants of Linear Programs
Matti Vahs, Shaohang Han, Jana Tumova
Control invariant sets play an important role in safety-critical control and find broad application in numerous fields such as obstacle avoidance for mobile robots. However, findin…
Forward Invariance in Trajectory Spaces for Safety-critical Control
Matti Vahs, Rafael I. Cabral Muchacho, Florian T. Pokorny +1
Useful robot control algorithms should not only achieve performance objectives but also adhere to hard safety constraints. Control Barrier Functions (CBFs) have been developed to p…
Belief Control Barrier Functions for Risk-aware Control
Matti Vahs, Christian Pek, Jana Tumova
Ensuring safety in real-world robotic systems is often challenging due to unmodeled disturbances and noisy sensor measurements. To account for such stochastic uncertainties, many r…
Risk-aware Control for Robots with Non-Gaussian Belief Spaces
Matti Vahs, Jana Tumova
This paper addresses the problem of safety-critical control of autonomous robots, considering the ubiquitous uncertainties arising from unmodeled dynamics and noisy sensors. To tak…
Non-smooth Control Barrier Functions for Stochastic Dynamical Systems
Matti Vahs, Jana Tumova
Uncertainties arising in various control systems, such as robots that are subject to unknown disturbances or environmental variations, pose significant challenges for ensuring syst…