103 citations · 181 across the 5 of their papers we have counts for
5 papers
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale
Yao Lu, Karol Hausman, Yevgen Chebotar +8
Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof autonomously collected experi…
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
Dhruv Shah, Peng Xu, Yao Lu +4
Reinforcement learning can train policies that effectively perform complex tasks. However for long-horizon tasks, the performance of these methods degrades with horizon, often nece…
ISTR: End-to-End Instance Segmentation with Transformers
Jie Hu, Liujuan Cao, Yao Lu +6
End-to-end paradigms significantly improve the accuracy of various deep-learning-based computer vision models. To this end, tasks like object detection have been upgraded by replac…
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Yevgen Chebotar, Karol Hausman, Yao Lu +8
We consider the problem of learning useful robotic skills from previously collected offline data without access to manually specified rewards or additional online exploration, a se…