3 curated papers, code, datasets and hardware from ByteDance (Seed / Research) (1 with code), with one-line takeaways.3 个来自字节跳动（Seed / Research）的精选论文、代码、数据集与硬件（1 个附代码），均配一句话要点。
Labs are matched from each entry's affiliation field by simple name rules (data/labs.yaml); joint work appears under every lab named. Only curated entries are listed, because daily arXiv papers carry no affiliations.机构由每个条目的机构字段按名称规则匹配（data/labs.yaml），合作工作会出现在所有署名机构下。仅列出精选条目，因为每日 arXiv 论文不含机构信息。
ByteDance Seed's generalist VLA: robot imitation data co-trained with web vision-language data, adapted quickly with VR-collected human trajectories; shown on long-horizon bimanual mobile tasks.字节跳动 Seed 的通用 VLA——机器人数据模仿学习并与网络视觉-语言数据联合训练，再用 VR 采集的人类轨迹快速适配；在 ByteMini 双臂移动机器人上展示长时程与衣物操作任务。
@misc{cheang2025gr,
title = {{GR-3 Technical Report}},
author = {Chilam Cheang and Sijin Chen and Zhongren Cui and Yingdong Hu and Liqun Huang and Tao Kong and Hang Li and Yifeng Li and Yuxiao Liu and Xiao Ma and Hao Niu and Wenxuan Ou and Wanli Peng and Zeyu Ren and Haixin Shi and Jiawen Tian and Hongtao Wu and Xin Xiao and Yuyang Xiao and Jiafeng Xu and Yichu Yang},
year = {2025},
eprint = {2507.15493},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2507.15493},
}
Pretrained to generate future video on 38M clips, then fine-tuned to predict frames and actions together; reports 97.7% average success over 100+ real tabletop tasks and roughly double GR-1's success in unseen scenes.先在 3800 万段视频上预训练未来帧生成，再在机器人数据上微调，同时预测视频帧与动作轨迹；报告在 100 多个真机桌面任务上平均成功率 97.7%，在未见场景中成功率约为 GR-1 的两倍。
@misc{cheang2024gr,
title = {{GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation}},
author = {Chi-Lam Cheang and Guangzeng Chen and Ya Jing and Tao Kong and Hang Li and Yifeng Li and Yuxiao Liu and Hongtao Wu and Jiafeng Xu and Yichu Yang and Hanbo Zhang and Minzhao Zhu},
year = {2024},
eprint = {2410.06158},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.06158},
}
GR-1 — a GPT-style model that predicts both actions and future frames; pre-training on Ego4D video prediction before robot fine-tuning lifts CALVIN unseen-scene success from 53.3% to 85.4% (as reported).GR-1：GPT 式模型同时预测动作与未来画面；先在 Ego4D 上做视频预测预训练、再用机器人数据微调，论文报告 CALVIN 未见场景成功率从 53.3% 提升到 85.4%。
@misc{wu2023unleashing,
title = {{Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation}},
author = {Hongtao Wu and Ya Jing and Chilam Cheang and Guangzeng Chen and Jiafeng Xu and Xinghang Li and Minghuan Liu and Hang Li and Tao Kong},
year = {2023},
eprint = {2312.13139},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.13139},
}