Shanghai Qi Zhi Institute · embodied AI & robot learning上海期智研究院 · 具身智能与机器人学习
6 curated papers, code, datasets and hardware from Shanghai Qi Zhi Institute (5 with code), with one-line takeaways.6 个来自上海期智研究院的精选论文、代码、数据集与硬件（5 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。
One model that decides when to think and when to act: it reasons at key moments (subtask done, error, human input) and otherwise acts on its latest reasoning; co-trained with synthetic reasoning-rich VL data.单一模型自主决定何时“想”、何时“做”：在子任务完成、出错或人类插话等关键时刻进行推理，其余时间依据最新推理结果生成动作；并与合成的推理型视觉-语言数据共同训练。
@misc{lin2025onetwovla,
title = {{OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning}},
author = {Fanqi Lin and Ruiqian Nai and Yingdong Hu and Jiacheng You and Junming Zhao and Yang Gao},
year = {2025},
eprint = {2505.11917},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.11917},
}
VPP — fine-tunes Stable Video Diffusion on robot and human videos and feeds its one-pass predictive features to a diffusion policy; reports 4.33 average length on CALVIN ABC→D and real Franka / dexterous-hand results.VPP：在机器人与人类操作视频上微调 Stable Video Diffusion，只做一次前向提取"预测性"视觉特征再交给扩散策略；论文报告 CALVIN ABC→D 平均完成长度 4.33，并有 Franka 与灵巧手真机实验。
@misc{hu2024video,
title = {{Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations}},
author = {Yucheng Hu and Yanjiang Guo and Pengchao Wang and Xiaoyu Chen and Yen-Jen Wang and Jianke Zhang and Koushil Sreenath and Chaochao Lu and Jianyu Chen},
year = {2024},
eprint = {2412.14803},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2412.14803},
}
Conditions a diffusion policy on a compact point-cloud encoding; more data-efficient and robust to viewpoint than images.用紧凑点云编码作为扩散策略条件，比图像输入更省数据、对视角更鲁棒。
@misc{ze2024d,
title = {{3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations}},
author = {Yanjie Ze and Gu Zhang and Kangning Zhang and Chenyuan Hu and Muhan Wang and Huazhe Xu},
year = {2024},
eprint = {2403.03954},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.03954},
}
Learns a vision-based whole-body parkour policy for the Unitree H1 without motion references, covering skills such as jumping onto platforms and leaping over gaps, and transfers it to the real robot.无需动作参考，为 Unitree H1 学习基于视觉的全身跑酷策略，涵盖跳上平台、跨越沟壑等技能，并迁移到真实机器人。
Distills specialist parkour skills into a single vision-based policy that climbs, leaps and crawls on low-cost quadrupeds.将多个跑酷专家技能蒸馏为单一视觉策略，让低成本四足机器人攀爬、跳跃和匍匐。
@misc{zhuang2023robot,
title = {{Robot Parkour Learning}},
author = {Ziwen Zhuang and Zipeng Fu and Jianren Wang and Christopher Atkeson and Soeren Schwertfeger and Chelsea Finn and Hang Zhao},
year = {2023},
eprint = {2309.05665},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.05665},
}
2.4M egocentric RGB-D frames (4,000+ sequences, 800 object instances in 16 categories, 610 rooms) of people handling rigid and articulated objects, with segmentation, hand and object pose labels.240 万帧第一视角 RGB-D（4000 多段序列、16 类 800 个物体实例、610 个房间），记录人操作刚体与铰接物体，带分割、手部与物体位姿标注。
@misc{liu2022hoi,
title = {{HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction}},
author = {Yunze Liu and Yun Liu and Che Jiang and Kangbo Lyu and Weikang Wan and Hao Shen and Boqiang Liang and Zhoujie Fu and He Wang and Li Yi},
year = {2022},
eprint = {2203.01577},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2203.01577},
}