630 citations · 1.2k across the 74 of their papers we have counts for
82 papers
Risk-Sensitive Inhibitory Control for Safe Reinforcement Learning
Armin Lederer, Erfaun Noorani, John S. Baras +1
Humans have the ability to deviate from their natural behavior when necessary, which is a cognitive process called response inhibition. Similar approaches have independently receiv…
Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors
Armin Lederer, Jonas Umlauft, Sandra Hirche
Due to the increasing complexity of technical systems, accurate first principle models can often not be obtained. Supervised machine learning can mitigate this issue by inferring m…
Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning
Xiaobing Dai, Armin Lederer, Zewen Yang +1
When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data. Gaussian process (GP) regression is a particularly…
Koopman Kernel Regression
Petar Bevanda, Max Beier, Armin Lederer +3
Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of intere…
Fast IMU-based Dual Estimation of Human Motion and Kinematic Parameters via Progressive In-Network Computing
Xiaobing Dai, Huanzhuo Wu, Siyi Wang +4
Many applications involve humans in the loop, where continuous and accurate human motion monitoring provides valuable information for safe and intuitive human-machine interaction.…
Estimating Motor Symptom Presence and Severity in Parkinson's Disease from Wrist Accelerometer Time Series using ROCKET and InceptionTime
Cedric Donié, Neha Das, Satoshi Endo +1
Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatme…