3 citations · 8 across the 22 of their papers we have counts for
4 papers · 1 filter
Maximum Total Correlation Reinforcement Learning
Bang You, Puze Liu, Huaping Liu +2
Simplicity is a powerful inductive bias. In reinforcement learning, regularization is used for simpler policies, data augmentation for simpler representations, and sparse reward fu…
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…
Morphologically Symmetric Reinforcement Learning for Ambidextrous Bimanual Manipulation
Zechu Li, Yufeng Jin, Daniel Ordonez Apraez +3
Humans naturally exhibit bilateral symmetry in their gross manipulation skills, effortlessly mirroring simple actions between left and right hands. Bimanual robots-which also featu…
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…