3 citations · 8 across the 22 of their papers we have counts for
7 papers · 1 filter
A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics
Puze Liu, Jonas Günster, Niklas Funk +17
Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges ass…
Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
Jonas Günster, Puze Liu, Jan Peters +1
Safety is one of the key issues preventing the deployment of reinforcement learning techniques in real-world robots. While most approaches in the Safe Reinforcement Learning area d…
Adaptive Control based Friction Estimation for Tracking Control of Robot Manipulators
Junning Huang, Davide Tateo, Puze Liu +1
Adaptive control is often used for friction compensation in trajectory tracking tasks because it does not require torque sensors. However, it has some drawbacks: first, the most co…
Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning
Piotr Kicki, Davide Tateo, Puze Liu +3
Trajectory planning under kinodynamic constraints is fundamental for advanced robotics applications that require dexterous, reactive, and rapid skills in complex environments. Thes…
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
Christopher E. Mower, Yuhui Wan, Hongzhan Yu +20
We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our sys…
Distilling Contact Planning for Fast Trajectory Optimization in Robot Air Hockey
Julius Jankowski, Ante Marić, Puze Liu +3
Robot control through contact is challenging as it requires reasoning over long horizons and discontinuous system dynamics. Highly dynamic tasks such as Air Hockey additionally req…
Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications
Puze Liu, Haitham Bou-Ammar, Jan Peters +1
Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. However, most existi…