6 papers · 1 filter
EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data
Baoyu Li, Xinchen Yin, Mengying Lin +2
Egocentric human data offers scalable supervision for robot manipulation. However, behavior cloning entangles transferable content like objects, scenes, and task semantics, with no…
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…
Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos
Kaifeng Zhang, Baoyu Li, Kris Hauser +1
Modeling the dynamics of deformable objects is challenging due to their diverse physical properties and the difficulty of estimating states from limited visual information. We addr…
SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation
Zekun Qi, Wenyao Zhang, Yufei Ding +15
While spatial reasoning has made progress in object localization relationships, it often overlooks object orientation-a key factor in 6-DoF fine-grained manipulation. Traditional p…
AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation
Kaifeng Zhang, Baoyu Li, Kris Hauser +1
Predictive models are a crucial component of many robotic systems. Yet, constructing accurate predictive models for a variety of deformable objects, especially those with unknown p…
Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning
Baoyu Li, William Edwards, Kris Hauser
AutoMPC is a Python package that automates and optimizes data-driven model predictive control. However, it can be computationally expensive and unstable when exploring large search…