Midea Group · embodied AI & robot learning美的集团 · 具身智能与机器人学习
3 curated papers, code, datasets and hardware from Midea Group (1 with code), with one-line takeaways.3 个来自美的集团的精选论文、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。
Keeps a VLA able to chat: phased alignment training plus a mixture-of-experts split to fight forgetting and task interference; reports strong VQA/MMMU scores and beats OpenVLA on 25 real tasks.让 VLA 保留“对话”能力：通过分阶段对齐训练和混合专家结构缓解遗忘与任务干扰；论文报告在保持 VQA/MMMU 等多模态理解成绩的同时，在 25 个真实操作任务上优于 OpenVLA。
@misc{zhou2025chatvla,
title = {{ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model}},
author = {Zhongyi Zhou and Yichen Zhu and Minjie Zhu and Junjie Wen and Ning Liu and Zhiyuan Xu and Weibin Meng and Ran Cheng and Yaxin Peng and Chaomin Shen and Feifei Feng},
year = {2025},
eprint = {2502.14420},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.14420},
}
Pairs a 2B Qwen2-VL with a ~1B plug-in diffusion action expert with one head per robot, trained in three stages (cross-embodiment, alignment, task); reports shirt folding without task-specific tuning and 97.3 on LIBERO.将 2B 的 Qwen2-VL 与约 1B 参数、按机器人分头输出的可插拔扩散动作专家结合，分三阶段训练（跨本体预训练、本体对齐、任务适配）；报告无需任务专门微调即可叠衬衫，LIBERO 平均 97.3。
@misc{wen2025dexvla,
title = {{DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control}},
author = {Junjie Wen and Yichen Zhu and Jinming Li and Zhibin Tang and Chaomin Shen and Feifei Feng},
year = {2025},
eprint = {2502.05855},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.05855},
}
A compact VLA built from a small multimodal model (up to 1.4B) with LoRA and a diffusion policy head, without large-scale robot pretraining; reports higher real-robot success than OpenVLA and ~20x faster inference.由小型多模态模型（至多 1.4B）加 LoRA 微调与扩散策略头构成的紧凑 VLA，无需大规模机器人预训练；报告真机成功率高于 OpenVLA，推理速度约快 20 倍。
@misc{wen2024tinyvla,
title = {{TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation}},
author = {Junjie Wen and Yichen Zhu and Jinming Li and Minjie Zhu and Kun Wu and Zhiyuan Xu and Ning Liu and Ran Cheng and Chaomin Shen and Yaxin Peng and Feifei Feng and Jian Tang},
year = {2024},
eprint = {2409.12514},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.12514},
}