17 citations · 54 across the 21 of their papers we have counts for
13 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…
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…
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…
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…
Floating-Base Deep Lagrangian Networks
Lucas Schulze, Juliano Decico Negri, Victor Barasuol +4
Grey-box methods for system identification combine deep learning with physics-informed constraints, capturing complex dependencies while improving out-of-distribution generalizatio…
Context-Aware Deep Lagrangian Networks for Model Predictive Control
Lucas Schulze, Jan Peters, Oleg Arenz
Controlling a robot based on physics-consistent dynamic models, such as Deep Lagrangian Networks (DeLaN), can improve the generalizability and interpretability of the resulting beh…
Learning Hierarchical Domain Models Through Environment-Grounded Interaction
Claudius Kienle, Benjamin Alt, Oleg Arenz +1
Domain models enable autonomous agents to solve long-horizon tasks by producing interpretable plans. However, in open-world environments, a single general domain model cannot captu…
Assisted Teleoperation in Changing Environments with a Mixture of Virtual Guides
Marco Ewerton, Oleg Arenz, Jan Peters
Haptic guidance is a powerful technique to combine the strengths of humans and autonomous systems for teleoperation. The autonomous system can provide haptic cues to enable the ope…
Deep Adversarial Reinforcement Learning for Object Disentangling
Melvin Laux, Oleg Arenz, Jan Peters +1
Deep learning in combination with improved training techniques and high computational power has led to recent advances in the field of reinforcement learning (RL) and to successful…
Probabilistic approach to physical object disentangling
Joni Pajarinen, Oleg Arenz, Jan Peters +1
Physically disentangling entangled objects from each other is a problem encountered in waste segregation or in any task that requires disassembly of structures. Often there are no…