7 papers
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…
Mask2Real-WM: Segmentation Masks as a Sim-to-Real Bridge for Controllable Dexterous World Models
Riccardo O. Feingold, Davide Liconti, Chenyu Yang +1
Action-conditioned world models allow robots to predict the future consequences of candidate actions without additional physical interaction, supporting policy evaluation, planning…
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…
A Benchmark of Dexterity for Anthropomorphic Robotic Hands
Davide Liconti, Yuning Zhou, Yasunori Toshimitsu +2
Dexterity is a central yet ambiguously defined concept in the design and evaluation of anthropomorphic robotic hands. In practice, the term is often used inconsistently, with diffe…
MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From Egocentric Videos
Alexey Gavryushin, Xi Wang, Robert J. S. Malate +5
Large-scale egocentric video datasets capture diverse human activities across a wide range of scenarios, offering rich and detailed insights into how humans interact with objects,…
Beyond Anthropomorphism: Enhancing Grasping and Eliminating a Degree of Freedom by Fusing the Abduction of Digits Four and Five
Simon Fritsch, Liam Achenbach, Riccardo Bianco +9
This paper presents the SABD hand, a 16-degree-of-freedom (DoF) robotic hand that departs from purely anthropomorphic designs to achieve an expanded grasp envelope, enable manipula…