5 papers · 1 filter
MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
Abhay Deshpande, Maya Guru, Rose Hendrix +23
A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or…
MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
Yejin Kim, Wilbert Pumacay, Omar Rayyan +23
Deploying robots at scale demands robustness to the long tail of everyday situations. The countless variations in scene layout, object geometry, and task specifications that charac…
GraspMolmo: Generalizable Task-Oriented Grasping via Large-Scale Synthetic Data Generation
Abhay Deshpande, Yuquan Deng, Arijit Ray +7
We present GrasMolmo, a generalizable open-vocabulary task-oriented grasping (TOG) model. GraspMolmo predicts semantically appropriate, stable grasps conditioned on a natural langu…
Data Efficient Behavior Cloning for Fine Manipulation via Continuity-based Corrective Labels
Abhay Deshpande, Liyiming Ke, Quinn Pfeifer +2
We consider imitation learning with access only to expert demonstrations, whose real-world application is often limited by covariate shift due to compounding errors during executio…
CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning
Liyiming Ke, Yunchu Zhang, Abhay Deshpande +2
We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding errors and disturbances. While existing…