16 citations · 73 across the 34 of their papers we have counts for
4 papers · 1 filter
Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions
Armin Lederer, Azra Begzadić, Neha Das +1
Ensuring safety is of paramount importance in physical human-robot interaction applications. This requires both adherence to safety constraints defined on the system state, as well…
Vision-Based Uncertainty-Aware Motion Planning based on Probabilistic Semantic Segmentation
Ralf Römer, Armin Lederer, Samuel Tesfazgi +1
For safe operation, a robot must be able to avoid collisions in uncertain environments. Existing approaches for motion planning under uncertainties often assume parametric obstacle…
Safe Reinforcement Learning via Confidence-Based Filters
Sebastian Curi, Armin Lederer, Sandra Hirche +1
Ensuring safety is a crucial challenge when deploying reinforcement learning (RL) to real-world systems. We develop confidence-based safety filters, a control-theoretic approach fo…
Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes
Armin Lederer, Mingmin Zhang, Samuel Tesfazgi +1
Safety-critical technical systems operating in unknown environments require the ability to quickly adapt their behavior, which can be achieved in control by inferring a model onlin…