From the 1 of 4 linked papers with an AI index.
4 papers
ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation
Nutan Chen, Jianxiang Feng, Marvin Alles +1
ConFlow incorporates task constraints directly into the training of flow-matching models for robot motion generation, using differentiable barrier functions, a conditional Gaussian…
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…