Generative pre-trained models have demonstrated remarkable effectiveness in
language and vision domains by learning useful representations. In this paper,
we extend the scope of this effectiveness by showing that visual robot
manipulation can significantly benefit from large-scale video generative
pre-training. We introduce GR-1, a straightforward GPT-style model designed for
multi-task language-conditioned visual robot manipulation. GR-1 takes as inputs
a language instruction, a sequence of observation images, and a sequence of
robot states. It predicts robot actions as well as future images in an
end-to-end manner. Thanks to a flexible design, GR-1 can be seamlessly
finetuned on robot data after pre-trained on a large-scale video dataset. We
perform extensive experiments on the challenging CALVIN benchmark and a real
robot. On CALVIN benchmark, our method outperforms state-of-the-art baseline
methods and improves the success rate from 88.9% to 94.9%. In the setting of
zero-shot unseen scene generalization, GR-1 improves the success rate from
53.3% to 85.4%. In real robot experiments, GR-1 also outperforms baseline
methods and shows strong potentials in generalization to unseen scenes and
objects. We provide inaugural evidence that a unified GPT-style transformer,
augmented with large-scale video generative pre-training, exhibits remarkable
generalization to multi-task visual robot manipulation. Project page:
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通过大规模视频生成预训练，我们展示了基于语言条件的视觉机器人操作对于生成预训练模型的有效性扩展，提供了新的证据，显示出在多任务视觉机器人操作中，经过视频生成预训练的统一 GPT 风格转换器具有显著的泛化能力。