5 citations · 8 across the 6 of their papers we have counts for
6 papers
Data Retrieval with Importance Weights for Few-Shot Imitation Learning
Amber Xie, Rahul Chand, Dorsa Sadigh +1
While large-scale robot datasets have propelled recent progress in imitation learning, learning from smaller task specific datasets remains critical for deployment in new environme…
Efficiently Generating Expressive Quadruped Behaviors via Language-Guided Preference Learning
Jaden Clark, Joey Hejna, Dorsa Sadigh
Expressive robotic behavior is essential for the widespread acceptance of robots in social environments. Recent advancements in learned legged locomotion controllers have enabled m…
Vision Language Models are In-Context Value Learners
Yecheng Jason Ma, Joey Hejna, Ayzaan Wahid +15
Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal…
So You Think You Can Scale Up Autonomous Robot Data Collection?
Suvir Mirchandani, Suneel Belkhale, Joey Hejna +3
A long-standing goal in robot learning is to develop methods for robots to acquire new skills autonomously. While reinforcement learning (RL) comes with the promise of enabling aut…
Distance Weighted Supervised Learning for Offline Interaction Data
Joey Hejna, Jensen Gao, Dorsa Sadigh
Sequential decision making algorithms often struggle to leverage different sources of unstructured offline interaction data. Imitation learning (IL) methods based on supervised lea…
Extreme Q-Learning: MaxEnt RL without Entropy
Divyansh Garg, Joey Hejna, Matthieu Geist +1
Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possibl…