7 citations · 24 across the 14 of their papers we have counts for
6 papers · 1 filter
SUAVE: Unified Video-Action Models via Masked Diffusion
Rhythm Syed, Jean Mercat, Sedrick Keh +5
Vision-language-action models (VLAs) inherit strong semantic grounding from pretrained vision-language backbones but are typically optimized for predicting actions rather than futu…
Cross-Embodiment Transfer via Behavior-Aligned Representations
Ajay Sridhar, Jensen Gao, Jonathan Yang +3
Recent progress in large-scale imitation learning for robot manipulation has been driven by leveraging datasets across a wide range of robot embodiments. However, achieving signifi…
VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
Jean Mercat, Sedrick Keh, Kushal Arora +5
We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize on the action training stage,…
A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation
Fanqi Lin, Kushal Arora, Jean Mercat +9
Large behavior models have shown strong dexterous manipulation capabilities by extending imitation learning to large-scale training on multi-task robot data, yet their generalizati…
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…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…