activity
20192024
most citedThinking While Moving: Deep Reinforcement Learning with Concurrent Control

9 citations · 26 across the 6 of their papers we have counts for

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21 papers · 1 filter

cs.RO202443 cited

OpenVLA: An Open-Source Vision-Language-Action Model

Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti +15

Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rath…

cs.RO20248 cited

Octo: An Open-Source Generalist Robot Policy

Octo Model Team, Dibya Ghosh, Homer Walke +16

Large policies pretrained on diverse robot datasets have the potential to transform robotic learning: instead of training new policies from scratch, such generalist robot policies…

cs.RO20244 cited

Evaluating Real-World Robot Manipulation Policies in Simulation

Xuanlin Li, Kyle Hsu, Jiayuan Gu +13

The field of robotics has made significant advances towards generalist robot manipulation policies. However, real-world evaluation of such policies is not scalable and faces reprod…

cs.RO20241 cited

RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches

Priya Sundaresan, Quan Vuong, Jiayuan Gu +10

Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over…

cs.RO2024

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…

cs.RO2024

Efficient Data Collection for Robotic Manipulation via Compositional Generalization

Jensen Gao, Annie Xie, Ted Xiao +2

Data collection has become an increasingly important problem in robotic manipulation, yet there still lacks much understanding of how to effectively collect data to facilitate broa…