6 citations · 11 across the 17 of their papers we have counts for
4 papers · 2 filters
Mask2Real-WM: Controllable Dexterous World Models via Segmentation Masks as a Sim-to-Real Bridge
Riccardo O. Feingold, Davide Liconti, Chenyu Yang +1
Learning action-conditioned world models for dexterous manipulation that are genuinely controllable requires capturing complex, high-dimensional hand kinematics from limited real-w…
Learning Robust Dexterous In-Hand Manipulation from Joint Sensors with Proprioceptive Transformer
Senlan Yao, Chenyu Yang, Jaehoon Kim +2
In-hand object manipulation is a fundamental yet challenging capability for dexterous robots. Despite significant progress in dexterous manipulation, existing approaches rely heavi…
EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World
Ryan Punamiya, Simar Kareer, Zeyi Liu +37
Robot learning increasingly depends on large and diverse data, yet robot data collection remains expensive and difficult to scale. Egocentric human data offer a promising alternati…
SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows
Chenyu Yang, Denis Tarasov, Davide Liconti +3
Real-world fine-tuning of dexterous manipulation policies remains challenging due to limited real-world interaction budgets and highly multimodal action distributions. Diffusion-ba…