9 citations · 12 across the 9 of their papers we have counts for
10 papers
Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees
Jakob Thumm, Marian Frei, Tianle Ni +2
Safe human-robot collaboration (HRC) requires accurate human pose estimation and motion prediction to prevent critical collisions. Existing certifiable safe HRC approaches are high…
From Demonstrations to Safe Deployment: Path-Consistent Safety Filtering for Diffusion Policies
Ralf Römer, Julian Balletshofer, Jakob Thumm +3
Diffusion policies (DPs) achieve state-of-the-art performance on complex manipulation tasks by learning from large-scale demonstration datasets, often spanning multiple embodiments…
Multi-Objective Causal Bayesian Optimization
Shriya Bhatija, Paul-David Zuercher, Jakob Thumm +1
In decision-making problems, the outcome of an intervention often depends on the causal relationships between system components and is highly costly to evaluate. In such settings,…
A General Safety Framework for Autonomous Manipulation in Human Environments
Jakob Thumm, Julian Balletshofer, Leonardo Maglanoc +2
Autonomous robots are projected to significantly augment the manual workforce, especially in repetitive and hazardous tasks. For a successful deployment of such robots in human env…
Text2Interaction: Establishing Safe and Preferable Human-Robot Interaction
Jakob Thumm, Christopher Agia, Marco Pavone +1
Adjusting robot behavior to human preferences can require intensive human feedback, preventing quick adaptation to new users and changing circumstances. Moreover, current approache…
Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking
Roland Stolz, Hanna Krasowski, Jakob Thumm +3
Continuous action spaces in reinforcement learning (RL) are commonly defined as multidimensional intervals. While intervals usually reflect the action boundaries for tasks well, th…
Human-Robot Gym: Benchmarking Reinforcement Learning in Human-Robot Collaboration
Jakob Thumm, Felix Trost, Matthias Althoff
Deep reinforcement learning (RL) has shown promising results in robot motion planning with first attempts in human-robot collaboration (HRC). However, a fair comparison of RL appro…
Reducing Safety Interventions in Provably Safe Reinforcement Learning
Jakob Thumm, Guillaume Pelat, Matthias Althoff
Deep Reinforcement Learning (RL) has shown promise in addressing complex robotic challenges. In real-world applications, RL is often accompanied by failsafe controllers as a last r…
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments
Jakob Thumm, Matthias Althoff
Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators. However, no method guarantees the safety of highly dynamic obstacles, such as h…
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking
Hanna Krasowski, Jakob Thumm, Marlon Müller +3
Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their potential for many real-world tasks. However, vanilla RL and most safe RL approaches do not…