activity
20202026
most citedEureka: Human-Level Reward Design via Coding Large Language Models

48 citations · 149 across the 20 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.RO2024

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…

cs.RO2024

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…

cs.RO2024

On-Robot Reinforcement Learning with Goal-Contrastive Rewards

Ondrej Biza, Thomas Weng, Lingfeng Sun +6

Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-r…

cs.CV2024★ 1 cited

Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model

Long Le, Jason Xie, William Liang +7

Interactive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation. However, creating these articulated objects…

cs.RO2024★ 7 cited

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…

cs.RO2024

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…

cs.RO2024★ 3 cited

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…