6 citations · 6 across the 8 of their papers we have counts for
11 papers
Auditable and Deployable Multi-Agent Motion Planning via Posetal Games from Legal Guidelines
Florian Lercher, Karan Mahesh, Yujun Huang +6
When robots are deployed in the real-world, they must interact with other agents and accomplish their own goals while adhering to legal constraints. These potentially conflicting o…
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…
Falsification-driven reinforcement learning for maritime motion planning
Marlon Müller, Florian Finkeldei, Hanna Krasowski +2
Compliance with maritime traffic rules is essential for the safe operation of autonomous vessels, yet training reinforcement learning (RL) agents to adhere to them is challenging.…
Safe Reinforcement Learning using Action Projection: Safeguard the Policy or the Environment?
Hannah Markgraf, Shambhuraj Sawant, Hanna Krasowski +3
Projection-based safety filters, which modify unsafe actions by mapping them to the closest safe alternative, are widely used to enforce safety constraints in reinforcement learnin…
Trajectory Planning with Signal Temporal Logic Costs using Deterministic Path Integral Optimization
Patrick Halder, Hannes Homburger, Lothar Kiltz +2
Formulating the intended behavior of a dynamic system can be challenging. Signal temporal logic (STL) is frequently used for this purpose due to its suitability in formalizing comp…
Intelligent Sailing Model for Open Sea Navigation
Hanna Krasowski, Stefan Schärdinger, Murat Arcak +1
Autonomous vessels potentially enhance safety and reliability of seaborne trade. To facilitate the development of autonomous vessels, simulations are required to model realistic in…
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…
Stepping Out of the Shadows: Reinforcement Learning in Shadow Mode
Philipp Gassert, Matthias Althoff
Reinforcement learning (RL) is not yet competitive for many cyber-physical systems, such as robotics, process automation, and power systems, as training on a system with physical c…
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…
Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea
Hanna Krasowski, Matthias Althoff
For safe operation, autonomous vehicles have to obey traffic rules that are set forth in legal documents formulated in natural language. Temporal logic is a suitable concept to for…