4 citations · 5 across the 13 of their papers we have counts for
13 papers
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…
BURNS: Backward Underapproximate Reachability for Neural-Feedback-Loop Systems
Chelsea Sidrane, Jana Tumova
Learning-enabled planning and control algorithms are increasingly popular, but they often lack rigorous guarantees of performance or safety. We introduce an algorithm for computing…
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…
CageCoOpt: Enhancing Manipulation Robustness through Caging-Guided Morphology and Policy Co-Optimization
Yifei Dong, Shaohang Han, Xianyi Cheng +5
Uncertainties in contact dynamics and object geometry remain significant barriers to robust robotic manipulation. Caging mitigates these uncertainties by constraining an object's m…
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…
TTT: A Temporal Refinement Heuristic for Tenuously Tractable Discrete Time Reachability Problems
Chelsea Sidrane, Jana Tumova
Reachable set computation is an important tool for analyzing control systems. Simulating a control system can show general trends, but a formal tool like reachability analysis can…
Robust STL Control Synthesis under Maximal Disturbance Sets
Joris Verhagen, Lars Lindemann, Jana Tumova
This work addresses maximally robust control synthesis under unknown disturbances. We consider a general nonlinear system, subject to a Signal Temporal Logic (STL) specification, a…
Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy
José Manuel Gaspar Sánchez, Leonard Bruns, Jana Tumova +2
Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measure…
Robust Active Measuring under Model Uncertainty
Merlijn Krale, Thiago D. Simão, Jana Tumova +1
Partial observability and uncertainty are common problems in sequential decision-making that particularly impede the use of formal models such as Markov decision processes (MDPs).…
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…