6 papers · 1 filter
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…
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…
Eurekaverse: Environment Curriculum Generation via Large Language Models
William Liang, Sam Wang, Hung-Ju Wang +3
Recent work has demonstrated that a promising strategy for teaching robots a wide range of complex skills is by training them on a curriculum of progressively more challenging envi…
DrEureka: Language Model Guided Sim-To-Real Transfer
Yecheng Jason Ma, William Liang, Hung-Ju Wang +5
Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. However, sim-to-real approaches typically rely on manual…
Eureka: Human-Level Reward Design via Coding Large Language Models
Yecheng Jason Ma, William Liang, Guanzhi Wang +6
Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation ta…
Composing Pre-Trained Object-Centric Representations for Robotics From "What" and "Where" Foundation Models
Junyao Shi, Jianing Qian, Yecheng Jason Ma +1
There have recently been large advances both in pre-training visual representations for robotic control and segmenting unknown category objects in general images. To leverage these…