17 citations · 54 across the 21 of their papers we have counts for
11 papers · 1 filter
Safe Reinforcement Learning with Receding-Horizon Safety Critic and Differentiable Action Shields
Siwei Ju, Jan Peters, Oleg Arenz
Despite the Markov assumption, safety failures in robot reinforcement learning rarely occur instantaneously; they typically emerge over several steps as the policy enters unfamilia…
A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning
Anish Diwan, Jan Peters, Oleg Arenz
Inverse Reinforcement Learning (IRL) is classically solved using Lagrangian optimisation, by jointly optimising a primal-dual problem with gradient descent to obtain the optimal re…
Residual Compliant Policy: Learning Compliant Policies from Few Demonstrations
Chunyu Jiang, Yichen Cai, Shuai Li +6
Contact-rich manipulation requires a robot to be precise in free space to establish the intended contacts, and compliant once in contact to avoid excessive forces. Imitation learni…
Continue, Abort, or Fall: Viability-Aware Policy Selection (VAPS) for Safe Humanoid Acrobatics
Siwei Ju, Lu Liu, Jan Peters +1
Dynamic humanoid motions such as flips risk hardware damage due to suboptimal policies, disturbances or sim-to-real gaps. A motion tracking policy offers no way out once the maneuv…
ExoLaN: Physics-Consistent Context-Aware Dynamics Learning for Exoskeletons
Lucas Schulze, Maximilian Schwarz, Jona Hoppe +2
Task-agnostic assistive exoskeleton control based on human intention offers greater flexibility than conventional approaches that rely on predefined tasks or motion patterns. Human…
Embedding Physics Priors in Robot Learning: A Survey
Mattia Piccinini, Lucas Schulze, Alice Plebe +10
The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remar…
Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates
Anish Diwan, Davide Tateo, Christopher E. Mower +3
Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories. Classical (dual-ascent) IRL guarante…
Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control
Siwei Ju, Jan Tauberschmidt, Oleg Arenz +2
Learning high-performance control policies that remain consistent with expert behavior is a fundamental challenge in robotics. Reinforcement learning can discover high-performing s…
Boosting deep Reinforcement Learning using pretraining with Logical Options
Zihan Ye, Phil Chau, Raban Emunds +5
Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed these challenges by encodin…
GaussTwin: Unified Simulation and Correction with Gaussian Splatting for Robotic Digital Twins
Yichen Cai, Paul Jansonnie, Cristiana de Farias +2
Digital twins promise to enhance robotic manipulation by maintaining a consistent link between real-world perception and simulation. However, most existing systems struggle with th…
Mathematical Foundations of Modeling ETL Process Chains
Levin Maier, Lucas Schulze, Robert Lilow +10
Extract-Transform-Load (ETL) processes are core components of modern data processing infrastructures. The throughput of processed data records can be adjusted by changing the amoun…