4 papers
Zero-Shot Generalization from Motion Demonstrations to New Tasks
Kilian Freitag, Alvin Combrink, Nadia Figueroa
Learning motion policies from expert demonstrations is an essential paradigm in modern robotics. While end-to-end models aim for broad generalization, they require large datasets a…
Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics
Kilian Freitag, Knut à kesson, Morteza Haghir Chehreghani
Deep Reinforcement Learning is a promising tool for robotic control, yet practical application is often hindered by the difficulty of designing effective reward functions. Real-wor…
Curriculum Reinforcement Learning for Complex Reward Functions
Kilian Freitag, Kristian Ceder, Rita Laezza +2
Reinforcement learning (RL) has emerged as a powerful tool for tackling control problems, but its practical application is often hindered by the complexity arising from intricate r…
Fine-tuning Myoelectric Control through Reinforcement Learning in a Game Environment
Kilian Freitag, Yiannis Karayiannidis, Jan Zbinden +1
Objective: Enhancing the reliability of myoelectric controllers that decode motor intent is a pressing challenge in the field of bionic prosthetics. State-of-the-art research has m…