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              Yafei Hu I’m a robotics researcher interested in 3D perception for robotics, and how motion planning and RL policy learning could benefit from 3D perception. I currenly work at RAI Institute (formerly known as Boston Dynamics AI Institute) as a research scientist. I did my PhD at Carnegie Mellon University, with Prof. Sebastian Scherer. If you are interested in doing an internship with me at RAI Institute, please shoot me an email. 
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           NovaFlow: Zero-Shot Manipulation via Actionable Flow from Generated Videos 
            arxiv /
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                @inproceedings{lisun2025novaflow,
                  abbr = {lisun2025novaflow},
                  title = {NovaFlow: Zero-Shot Manipulation via Actionable Flow from Generated Videos},
                  author={Hongyu Li* and Lingfeng Sun* and Yafei Hu and Duy Ta and Jennifer Barry and George Konidaris and Jiahui Fu},
                  year={2025},
                  eprint={2510.08568},
                  archivePrefix={arXiv},
                  primaryClass={cs.RO},
                  url={https://arxiv.org/abs/2510.08568},
                  bibtex_show = {true},
                  selected = {true}
                }
            
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           Learning Generalizable Feature Fields for Mobile Manipulation 
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                @inproceedings{qiuhu2024geff,
                  abbr = {qiuhu2024geff},
                  title = {Learning Generalizable Feature Fields for Mobile Manipulation},
                  author = {Qiu*, Ri-Zhao and Hu*, Yafei and and Song, Yuchen*, Yang, Ge and Fu, Yang and Ye, 
                    Jianglong and Mu, Jiteng and Yang, Ruihan and Atanasov, Nikolay and Scherer, Sebastian and Wang, Xiaolong},
                  year = {2025},
                  booktitle = {IROS},
                  html = {https://geff-b1.github.io/},
                  pdf = {https://arxiv.org/pdf/2403.07563.pdf},
                  bibtex_show = {true},
                  selected = {true}
                }
            
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           Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis 
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                @inproceedings{hu2023robofm,
                  abbr = {hu2023robofm},
                  title = {Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis},
                  author = {Hu*, Yafei and Xie*, Quanting and Jain*, Vidhi and Francis, Jonathan and Patrikar, Jay and Keetha, Nikhil and 
                    Kim, Seungchan and Xie, Yaqi and Zhang, Tianyi and Zhao, Zhibo and Chong, Yu-Quan and Wang, Chen and Sycara, Katia and 
                    Johnson-Roberson, Matthew and Batra, Dhruv and Wang, Xiaolong and Scherer, Sebastian and Kira, Zsolt and Xia, Fei and Bisk, Yonatan},
                  year = {2023},
                  booktitle = {arXiv:2312.08782},
                  html = {https://robotics-fm-survey.github.io/},
                  pdf = {https://arxiv.org/pdf/2312.08782.pdf},
                  bibtex_show = {true},
                  selected = {true}
                }
            
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            Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments 
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                @inproceedings{hu2023opere,
                  abbr = {hu2023opere},
                  title = {Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments},
                  author = {Hu, Yafei and Geng, Junyi and Wang, Chen and Keller, John and Scherer, Sebastian},
                  booktitle = {IEEE Robotics and Automation Letters (RA-L)},
                  year = {2023},
                  html = {https://jeffreyyh.github.io/opere/},
                  pdf = {https://arxiv.org/pdf/2204.03140.pdf},
                  code = {},
                  bibtex_show = {true},
                  selected = {true}
                }
            
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            Unsupervised Online Learning for Robotic Interestingness with Visual Memory 
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                @inproceedings{2021_TRO_Inter,
                  abbr = {2021_TRO_Inter},
                  title = {Unsupervised Online Learning for Robotic Interestingness with Visual Memory},
                  author = {Wang, Chen and Qiu, Yuheng and Wang, Wenshan and Hu, Yafei and Kim, Seungchan and Scherer, Sebastian},
                  booktitle = {IEEE Transactions on Robotics},
                  year = {2021},
                  html = {https://arxiv.org/abs/2111.09793},
                  pdf = {https://arxiv.org/pdf/2111.09793.pdf},
                  code = {},
                  bibtex_show = {true},
                  selected = {true}
                }
            
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            Visual Memorability for Robotic Interestingness via Unsupervised Online Learning 
            arxiv /
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              @inproceedings{2020_ECCV_Inter,
                abbr = {2020_ECCV_Inter},
                title = {Visual Memorability for Robotic Interestingness via Unsupervised Online Learning},
                author = {Wang, Chen and Wang, Wenshan and Qiu, Yuheng and Hu, Yafei and Scherer, Sebastian},
                booktitle = {ECCV},
                year = {2020},
                html = {https://arxiv.org/abs/2005.08829},
                pdf = {https://arxiv.org/pdf/2005.08829.pdf},
                code = {},
                bibtex_show = {true},
                selected = {true}
              }
            
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            TartanAir: A Dataset to Push the Limits of Visual SLAM 
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              @inproceedings{2020_IROS_TartanAir,
                abbr = {2020_IROS_TartanAir},
                title = {TartanAir: A Dataset to Push the Limits of Visual SLAM},
                author = {Wang, Wenshan and Zhu, Delong and Wang, Xiangwei and Hu, Yaoyu and Qiu, Yuheng and Wang, Chen and Hu, Yafei and Kapoor, Ashish and Scherer, Sebastian},
                booktitle = {IROS},
                year = {2020},
                html = {https://arxiv.org/abs/2003.14338},
                pdf = {https://arxiv.org/pdf/2003.14338.pdf},
                code = {},
                bibtex_show = {true},
                selected = {true}
              }
            
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Stolen from Georgia Gkioxari