630 citations · 1.2k across the 74 of their papers we have counts for
8 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…
Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception
Hendrik Borner, Gerolamo Carboni, Xiaoxiao Cheng +4
When moving a piano or dancing tango with a partner, how should I control my arm muscles to best feel their movements and follow or guide them smoothly? Here we observe how physica…
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…
Dext-Gen: Dexterous Grasping in Sparse Reward Environments with Full Orientation Control
Martin Schuck, Jan Brüdigam, Alexandre Capone +2
Reinforcement learning is a promising method for robotic grasping as it can learn effective reaching and grasping policies in difficult scenarios. However, achieving human-like man…
Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes
Giulio Evangelisti, Sandra Hirche
This paper proposes a physically consistent Gaussian Process (GP) enabling the identification of uncertain Lagrangian systems. The function space is tailored according to the energ…