Georgia Tech · embodied AI & robot learning佐治亚理工学院 · 具身智能与机器人学习
5 curated papers, code, datasets and hardware from Georgia Tech (4 with code), with one-line takeaways.5 个来自佐治亚理工学院的精选论文、代码、数据集与硬件（4 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。
MetaGenSim learns which simulated data to generate, using meta-gradients from real-world performance, instead of sampling the simulator uniformly.MetaGenSim 利用来自真实世界表现的元梯度，学习应该生成哪些仿真数据，而不是对仿真器做均匀采样。
@misc{ma2026active,
title = {{Active Generative Simulation: Meta-Learning What to Simulate for Sim-and-Real Policy Co-Training}},
author = {Liqian Ma and Vaibhav Saxena and Utkarsh Aashu Mishra and Shuo Cheng and Yongxin Chen and Danfei Xu},
year = {2026},
eprint = {2610.19479},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.19479},
}
Treats egocentric human video with 3D hand tracking (Project Aria glasses) as robot data and co-trains one policy on both; reports an extra hour of human hand data helps more than an extra hour of robot data.把佩戴 Project Aria 眼镜采集、带三维手部跟踪的第一视角人类视频当作机器人数据，与机器人数据共同训练同一个策略；论文报告多 1 小时人手数据比多 1 小时机器人数据更有用。
@misc{kareer2024egomimic,
title = {{EgoMimic: Scaling Imitation Learning via Egocentric Video}},
author = {Simar Kareer and Dhruv Patel and Ryan Punamiya and Pranay Mathur and Shuo Cheng and Chen Wang and Judy Hoffman and Danfei Xu},
year = {2024},
eprint = {2410.24221},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24221},
}
PIRLNav — behavior cloning on human ObjectNav demonstrations, then RL fine-tuning of the same policy; the paper reports 65.0% success on Habitat ObjectNav, 5 points above the previous best.PIRLNav：先在人类 ObjectNav 演示上做行为克隆，再用强化学习微调同一策略；论文报告在 Habitat ObjectNav 上成功率 65.0%，比此前最佳高 5 个百分点。
@misc{ramrakhya2023pirlnav,
title = {{PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav}},
author = {Ram Ramrakhya and Dhruv Batra and Erik Wijmans and Abhishek Das},
year = {2023},
eprint = {2301.07302},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2301.07302},
}
Zero-shot object-goal navigation that scores exploration frontiers by vision-language similarity to the target, with no task-specific training; tested in Habitat and on a Spot robot.零样本目标物体导航：用视觉-语言模型对探索前沿与目标的相关度打分来决定往哪走，无需针对任务训练；在 Habitat 和 Spot 机器人上验证。
@misc{yokoyama2023vlfm,
title = {{VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation}},
author = {Naoki Yokoyama and Sehoon Ha and Dhruv Batra and Jiuguang Wang and Bernadette Bucher},
year = {2023},
eprint = {2312.03275},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.03275},
}
VLN-CE moves Room-to-Room instructions from a fixed navigation graph into continuous Habitat scenes with low-level actions; the standard setting for realistic VLN.VLN-CE 把 Room-to-Room 指令从固定导航图搬到 Habitat 连续环境中、使用底层动作执行，是更贴近真实的 VLN 标准设定。
@misc{krantz2020beyond,
title = {{Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments}},
author = {Jacob Krantz and Erik Wijmans and Arjun Majumdar and Dhruv Batra and Stefan Lee},
year = {2020},
eprint = {2004.02857},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2004.02857},
}