Robbyant · embodied AI & robot learning蚂蚁灵波 · 具身智能与机器人学习
3 curated papers, code, datasets and hardware from Robbyant (3 with code), with one-line takeaways.3 个来自蚂蚁灵波的精选论文、代码、数据集与硬件（3 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。
LingBot-VLA: an Apache-2.0 foundation model trained on about 20,000 hours of real data from nine dual-arm setups, tested on four platforms with 100 tasks each; code, base model and benchmark data are released.LingBot-VLA：Apache-2.0 开源的基础模型，用九种双臂构型约 2 万小时真实数据训练，并在四个平台各 100 项任务上评测；代码、基座模型和评测数据一并开放。
@misc{wu2026pragmatic,
title = {{A Pragmatic VLA Foundation Model}},
author = {Wei Wu and Fan Lu and Yunnan Wang and Shuai Yang and Shi Liu and Fangjing Wang and Qian Zhu and He Sun and Yong Wang and Shuailei Ma and Yiyu Ren and Kejia Zhang and Hui Yu and Jingmei Zhao and Shuai Zhou and Zhenqi Qiu and Houlong Xiong and Ziyu Wang and Zechen Wang and Ran Cheng and Yong-Lu Li and Yongtao Huang and Xing Zhu and Yujun Shen and Kecheng Zheng},
year = {2026},
eprint = {2601.18692},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2601.18692},
}
LingBot-VA: an autoregressive diffusion framework that learns frame prediction and policy together, with shared video-action latents, closed-loop rollout and asynchronous inference.LingBot-VA：同时学习帧预测和策略的自回归扩散框架，具有视频动作共享潜空间、闭环推演和异步推理。
@misc{li2026causal,
title = {{Causal World Modeling for Robot Control}},
author = {Lin Li and Qihang Zhang and Yiming Luo and Shuai Yang and Ruilin Wang and Fei Han and Mingrui Yu and Zelin Gao and Nan Xue and Xing Zhu and Yujun Shen and Yinghao Xu},
year = {2026},
eprint = {2601.21998},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2601.21998},
}
Performs unseen manipulation tasks by following a human video placed in context, with no parameter update; it turns robot trajectories into matched human videos to get paired training data.通过跟随上下文中的人类视频执行未见过的操作任务，无需更新参数；并把机器人轨迹自动转成匹配的人类视频，以获得成对训练数据。
@misc{zhou2026zero,
title = {{Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization}},
author = {Jiaming Zhou and Qihang Zhang and Gangwei Xu and Cunxin Fan and Yujie Zhao and Ruilin Wang and Yiming Luo and Shuai Yang and Xing Zhu and Yujun Shen and Junwei Liang and Yinghao Xu},
year = {2026},
eprint = {2608.26103},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2608.26103},
}