623 citations · 738 across the 15 of their papers we have counts for
15 papers
ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation
Nutan Chen, Jianxiang Feng, Marvin Alles +1
In recent years Flow Matching has become a prominent method for generative modeling robot motion generation. In its generic form Flow Matching is an ODE-based neural sampler that i…
Latent Matters: Learning Deep State-Space Models
Alexej Klushyn, Richard Kurle, Maximilian Soelch +2
Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by maximising the evidence lower b…
FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning
Marvin Alles, Nutan Chen, Patrick van der Smagt +1
The use of guidance to steer sampling toward desired outcomes has been widely explored within diffusion models, especially in applications such as image and trajectory generation.…
Guided Decoding for Robot On-line Motion Generation and Adaption
Nutan Chen, Botond Cseke, Elie Aljalbout +3
We present a novel motion generation approach for robot arms, with high degrees of freedom, in complex settings that can adapt online to obstacles or new via points. Learning from…
Local Distance Preserving Auto-encoders using Continuous k-Nearest Neighbours Graphs
Nutan Chen, Patrick van der Smagt, Botond Cseke
Auto-encoder models that preserve similarities in the data are a popular tool in representation learning. In this paper we introduce several auto-encoder models that preserve local…
Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation
Felix Frank, Alexandros Paraschos, Patrick van der Smagt +1
Placing robots outside controlled conditions requires versatile movement representations that allow robots to learn new tasks and adapt them to environmental changes. The introduct…