GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation

• URL: http://arxiv.org/abs/2410.06158v1
• Date: Tue, 8 Oct 2024 16:00:47 GMT
• Title: GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• Authors: Chi-Lam Cheang, Guangzeng Chen, Ya Jing, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Hongtao Wu, Jiafeng Xu, Yichu Yang, Hanbo Zhang, Minzhao Zhu,
• Abstract summary: GR-2 is a state-of-the-art generalist robot agent for versatile and generalizable manipulation. GR-2 is first pre-trained on a vast number of Internet videos to capture the dynamics of the world. GR-2 exhibits impressive multi-task learning capabilities, achieving an average success rate of 97.7% across more than 100 tasks.
• Score: 21.455124378231957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present GR-2, a state-of-the-art generalist robot agent for versatile and generalizable robot manipulation. GR-2 is first pre-trained on a vast number of Internet videos to capture the dynamics of the world. This large-scale pre-training, involving 38 million video clips and over 50 billion tokens, equips GR-2 with the ability to generalize across a wide range of robotic tasks and environments during subsequent policy learning. Following this, GR-2 is fine-tuned for both video generation and action prediction using robot trajectories. It exhibits impressive multi-task learning capabilities, achieving an average success rate of 97.7% across more than 100 tasks. Moreover, GR-2 demonstrates exceptional generalization to new, previously unseen scenarios, including novel backgrounds, environments, objects, and tasks. Notably, GR-2 scales effectively with model size, underscoring its potential for continued growth and application. Project page: \url{https://gr2-manipulation.github.io}.

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