2 citations · 3 across the 8 of their papers we have counts for
8 papers
Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards
Christian Scherer, Joe Watson, Theo Gruner +3
Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for…
Towards Safe Robot Foundation Models Using Inductive Biases
Maximilian Tölle, Theo Gruner, Daniel Palenicek +6
Safety is a critical requirement for the real-world deployment of robotic systems. Unfortunately, while current robot foundation models show promising generalization capabilities a…
Towards Safe Robot Foundation Models
Maximilian Tölle, Theo Gruner, Daniel Palenicek +5
Robot foundation models hold the potential for deployment across diverse environments, from industrial applications to household tasks. While current research focuses primarily on…
sbi reloaded: a toolkit for simulation-based inference workflows
Jan Boelts, Michael Deistler, Manuel Gloeckler +30
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a…
Analysing the Interplay of Vision and Touch for Dexterous Insertion Tasks
Janis Lenz, Theo Gruner, Daniel Palenicek +2
Robotic insertion tasks remain challenging due to uncertainties in perception and the need for precise control, particularly in unstructured environments. While humans seamlessly c…
Machine Learning with Physics Knowledge for Prediction: A Survey
Joe Watson, Chen Song, Oliver Weeger +12
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential e…
Learning Tactile Insertion in the Real World
Daniel Palenicek, Theo Gruner, Tim Schneider +5
Humans have exceptional tactile sensing capabilities, which they can leverage to solve challenging, partially observable tasks that cannot be solved from visual observation alone.…
Pseudo-Likelihood Inference
Theo Gruner, Boris Belousov, Fabio Muratore +2
Simulation-Based Inference (SBI) is a common name for an emerging family of approaches that infer the model parameters when the likelihood is intractable. Existing SBI methods eith…