X Square Robot · embodied AI & robot learning自变量机器人 · 具身智能与机器人学习
3 curated papers, code, datasets and hardware from X Square Robot (3 with code), with one-line takeaways.3 个来自自变量机器人的精选论文、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。
Uses semantically coherent action events rather than fixed-length chunks as the unit of learning, to resolve the timescale mismatch between language, vision and control.用语义完整的动作事件代替固定长度动作块作为学习单元，以解决语言、视觉和控制之间的时间尺度不匹配。
@misc{li2026wall,
title = {{WALL-WM: Carving World Action Modeling at the Event Joints}},
author = {Shalfun Li and Victor Yao and Charles Yang and Truth Qu and Regis Cheng and Ryan Yu and Howard Lu and Newton Von and Vincent Chen and Yohann Tang and Maeve Zhang and Ellie Ma and Gody Li and Starrick Liu and Sage Yang and Lorien Shu and J. W. Gao and Ethan Chen and Colin Ye and Yu Sun and Elise Mon and PS Zhang and Neo Li and Lily Li and James Wang and Ping Yang and Chris Pan and Lucy Liang and Hang Su and Roy Gan and Hao Wang and Qian Wang},
year = {2026},
eprint = {2606.01955},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2606.01955},
}
Wall-OSS-0.5: open 4B VLA on a Qwen2.5-VL-3B backbone, pretrained across 20+ embodiments; reports 60.5% task progress after fine-tuning on 15 real tasks, against 43.0% for π0.5.Wall-OSS-0.5：自变量机器人（X Square Robot）开源的 40 亿参数 VLA 模型，基于 Qwen2.5-VL-3B 视觉语言主干，在 20 余种机器人形态上预训练；报告在 15 个真机任务上微调后平均任务进度 60.5%，π0.5 为 43.0%。
@misc{yu2026wall,
title = {{Wall-OSS-0.5 Technical Report}},
author = {Ryan Yu and Pushi Zhang and Starrick Liu and Brae Liu and Miracle Kang and Shalfun Li and Lights Shi and Ellie Ma and Ping Yang and Chris Pan and Jerry Chen and Dongxiu Liu and Rain Sun and Miles Guo and Byron Zhang and Hugo Zhou and Zach Xu and Vincent Chen and Harrison Huang and James Wang and Dance Kuzi and Andy Zhai and Hang Su and Roy Gan and Lucy Liang and Hao Wang and Qian Wang},
year = {2026},
eprint = {2605.30877},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2605.30877},
}
WALL-OSS: an open embodied foundation model that links instruction reasoning, subgoal decomposition and action generation in one differentiable model ('unified cross-level CoT'); training code and checkpoints are public.WALL-OSS：开源具身基础模型，在同一可微模型中串联指令推理、子目标分解与细粒度动作生成（"统一跨层级思维链"）；训练代码与模型权重已开源。
@misc{zhai2025igniting,
title = {{Igniting VLMs toward the Embodied Space}},
author = {Andy Zhai and Brae Liu and Bruno Fang and Chalse Cai and Ellie Ma and Ethan Yin and Hao Wang and Hugo Zhou and James Wang and Lights Shi and Lucy Liang and Make Wang and Qian Wang and Roy Gan and Ryan Yu and Shalfun Li and Starrick Liu and Sylas Chen and Vincent Chen and Zach Xu},
year = {2025},
eprint = {2509.11766},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2509.11766},
}