312 citations · 414 across the 8 of their papers we have counts for
11 papers
STARE-VLA: Progressive Stage-Aware Reinforcement for Fine-Tuning Vision-Language-Action Models
Feng Xu, Guangyao Zhai, Xin Kong +4
Recent advances in Vision-Language-Action (VLA) models, powered by large language models and reinforcement learning-based fine-tuning, have shown remarkable progress in robotic man…
What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
Kiana Ehsani, Daniel Gordon, Thomas Nguyen +2
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approache…
Watching the World Go By: Representation Learning from Unlabeled Videos
Daniel Gordon, Kiana Ehsani, Dieter Fox +1
Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks. The basic principle in these works is instance discrimination: le…
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon +5
We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to seque…
SplitNet: Sim2Sim and Task2Task Transfer for Embodied Visual Navigation
Daniel Gordon, Abhishek Kadian, Devi Parikh +2
We propose SplitNet, a method for decoupling visual perception and policy learning. By incorporating auxiliary tasks and selective learning of portions of the model, we explicitly…
What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning
Daniel Gordon, Dieter Fox, Ali Farhadi
Long-term planning poses a major difficulty to many reinforcement learning algorithms. This problem becomes even more pronounced in dynamic visual environments. In this work we pro…