4 citations · 10 across the 17 of their papers we have counts for
18 papers
Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control
Zihan Ye, Jiayi Liu, Puze Liu +4
As robots are increasingly deployed in everyday environments, ensuring their safety has become a central challenge. Existing methods often encode safety requirements as opaque math…
Blind Dexterity: Whole-Body Humanoid Manipulation via Pure Proprioception
Aditya Bhatt, Oleg Kaidanov, Puze Liu +1
We present blind, whole-body manipulation skills on a Unitree G1 humanoid using only onboard proprioception, without cameras, markers, force-torque, or tactile sensors. Despite thi…
Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
Paolo Magliano, Puze Liu, Jan Peters +2
Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation. However, real-world deployment in ope…
Towards Safe Robot Foundation Models Using Inductive Biases
Maximilian Tölle, Theo Gruner, Daniel Palenicek +6
Safety is a critical requirement for the real-world deployment of robotic systems. Unfortunately, while current robot foundation models show promising generalization capabilities a…
Towards Safe Robot Foundation Models
Maximilian Tölle, Theo Gruner, Daniel Palenicek +5
Robot foundation models hold the potential for deployment across diverse environments, from industrial applications to household tasks. While current research focuses primarily on…
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…
Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fields
Felix Herrmann, Sebastian Zach, Jacopo Banfi +3
Estimating collision probabilities between robots and environmental obstacles or other moving agents is crucial to ensure safety during path planning. This is an important building…
One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion
Nico Bohlinger, Grzegorz Czechmanowski, Maciej Krupka +4
Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped,…
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…
Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotion
Henri-Jacques Geiß, Firas Al-Hafez, Andre Seyfarth +2
Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation and high-dimensional action space. While many reinforcement learning methods atte…
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…
LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion
Firas Al-Hafez, Guoping Zhao, Jan Peters +1
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents. However, many existing locomotion benchmarks primarily focus on simplified toy tasks,…
Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot
Luca Lach, Robert Haschke, Davide Tateo +4
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation ta…
Time-Efficient Reinforcement Learning with Stochastic Stateful Policies
Firas Al-Hafez, Guoping Zhao, Jan Peters +1
Stateful policies play an important role in reinforcement learning, such as handling partially observable environments, enhancing robustness, or imposing an inductive bias directly…