4 citations · 5 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
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…