1 citations · 1 across the 6 of their papers we have counts for
6 papers
IMLE-VLA: Fast Single-Step Action Generation for Vision-Language-Action Policies
Kian Hosseinkhani, Qinhe Peng, George Shramko +6
Vision-language-action (VLA) policies leverage pretrained vision-language backbones to achieve strong cross-task generalization. A leading design couples this backbone with a dedic…
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
Jianing Qian, Qinhe Peng, Emmanuel Panov +4
Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due t…
Tether: Autonomous Functional Play with Correspondence-Driven Trajectory Warping
William Liang, Sam Wang, Hung-Ju Wang +3
The ability to conduct and learn from interaction and experience is a central challenge in robotics, offering a scalable alternative to labor-intensive human demonstrations. Howeve…
RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +1
Multi-task ``vision-language-action'' (VLA) models have recently demonstrated increasing promise as generalist foundation models for robotics, achieving non-trivial performance out…
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +1
Building generalist agents that can rapidly adapt to new environments is a key challenge for deploying AI in the digital and real worlds. Is scaling current agent architectures the…
Memory-Consistent Neural Networks for Imitation Learning
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +2
Imitation learning considerably simplifies policy synthesis compared to alternative approaches by exploiting access to expert demonstrations. For such imitation policies, errors aw…