604 citations · 1.7k across the 67 of their papers we have counts for
67 papers
Cross-Modal Visuo-Tactile Object Perception
Anirvan Dutta, Simone Tasciotti, Claudia Cusseddu +6
Estimating physical properties is critical for safe and efficient autonomous robotic manipulation, particularly during contact-rich interactions. In such settings, vision and tacti…
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…
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…
TechOps: Technical Documentation Templates for the AI Act
Laura Lucaj, Alex Loosley, Hakan Jonsson +2
Operationalizing the EU AI Act requires clear technical documentation to ensure AI systems are transparent, traceable, and accountable. Existing documentation templates for AI syst…
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…