1 citations · 1 across the 4 of their papers we have counts for
5 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 Action World Models for Control with Unlabeled Trajectories
Marvin Alles, Xingyuan Zhang, Patrick van der Smagt +1
Inspired by how humans combine direct interaction with action-free experience (e.g., videos), we study world models that learn from heterogeneous data. Standard world models typica…
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.…
Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
Marvin Alles, Philip Becker-Ehmck, Patrick van der Smagt +1
In offline reinforcement learning, a policy is learned using a static dataset in the absence of costly feedback from the environment. In contrast to the online setting, only using…
Learning to Centralize Dual-Arm Assembly
Marvin Alles, Elie Aljalbout
Robotic manipulators are widely used in modern manufacturing processes. However, their deployment in unstructured environments remains an open problem. To deal with the variety, co…