9 papers
One Demo is Worth a Thousand Trajectories: Action-View Augmentation for Visuomotor Policies
Chuer Pan, Litian Liang, Dominik Bauer +4
Visuomotor policies for manipulation have demonstrated remarkable potential in modeling complex robotic behaviors, yet minor alterations in the robot's initial configuration and un…
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
Yi Wang, Xinchen Li, Pengwei Xie +13
Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter dist…
Geometry-aware 4D Video Generation for Robot Manipulation
Zeyi Liu, Shuang Li, Eric Cousineau +3
Understanding and predicting dynamics of the physical world can enhance a robot's ability to plan and interact effectively in complex environments. While recent video generation mo…
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu +79
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently…
Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets
Chuning Zhu, Raymond Yu, Siyuan Feng +3
Imitation learning has emerged as a promising approach towards building generalist robots. However, scaling imitation learning for large robot foundation models remains challenging…
PolyTouch: A Robust Multi-Modal Tactile Sensor for Contact-rich Manipulation Using Tactile-Diffusion Policies
Jialiang Zhao, Naveen Kuppuswamy, Siyuan Feng +2
Achieving robust dexterous manipulation in unstructured domestic environments remains a significant challenge in robotics. Even with state-of-the-art robot learning methods, haptic…