Yunfan Jiang

All Publications

• BEHAVIOR ROBOT SUITE: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities Jiang, Y., Zhang, R., Wong, J., Wang, C., Ze, Y., Yin, H., Gokmen, C., Song, S., Wu, J., Li Fei-Fei edited by Lim, J., Song, S., Park, H. W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2025: 1246-1281

  View details for Web of Science ID 001676333500059

• Open X-Embodiment: Robotic Learning Datasets and RT-X Models O'Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., Tung, A., Bewley, A., Herzog, A., Irpan, A., Khazatsky, A., Rai, A., Gupta, A., Wang, A., Kolobov, A., Singh, A., Garg, A., Kembhavi, A., Xie, A., Brohan, A., Raffin, A., Sharma, A., Yavary, A., Jain, A., Balakrishna, A., Wahid, A., Burgess-Limerick, B., Kim, B., Scholkopf, B., Wulfe, B., Ichter, B., Lu, C., Xu, C., Le, C., Finn, C., Wang, C., Xu, C., Chi, C., Huang, C., Chan, C., Agia, C., Pan, C., Fu, C., Devin, C., Xu, D., Morton, D., Driess, D., Chen, D., Pathak, D., Shah, D., Buchler, D., Jayaraman, D., Kalashnikov, D., Sadigh, D., Johns, E., Foster, E., Liu, F., Ceola, F., Xia, F., Zhao, F., Frujeri, F., Stulp, F., Zhou, G., Sukhatme, G. S., Salhotra, G., Yan, G., Feng, G., Schiavi, G., Berseth, G., Kahn, G., Yang, G., Wang, G., Su, H., Fang, H., Shi, H., Bao, H., Ben Amor, H., Christensen, H., Furuta, H., Bharadhwaj, H., Walke, H., Fang, H., Ha, H., Mordatch, I., Radosavovic, I., Leal, I., Liang, J., Abou-Chakra, J., Kim, J., Drake, J., Peters, J., Schneider, J., Hsu, J., Vakil, J., Bohg, J., Bingham, J., Wu, J., Gao, J., Hu, J., Wu, J., Wu, J., Sun, J., Luo, J., Gu, J., Tan, J., Oh, J., Wu, J., Lu, J., Yang, J., Malik, J., Silverio, J., Hejna, J., Booher, J., Tompson, J., Yang, J., Salvador, J., Lim, J. J., Han, J., Wang, K., Rao, K., Pertsch, K., Hausman, K., Go, K., Gopalakrishnan, K., Goldberg, K., Byrne, K., Oslund, K., Kawaharazuka, K., Black, K., Lin, K., Zhang, K., Ehsani, K., Lekkala, K., Ellis, K., Rana, K., Srinivasan, K., Fang, K., Singh, K., Zeng, K., Hatch, K., Hsu, K., Itti, L., Chen, L., Pinto, L., Li Fei-Fei, Tan, L., Fan, L., Ott, L., Lee, L., Weihs, L., Chen, M., Lepert, M., Memmel, M., Tomizuka, M., Itkina, M., Castro, M., Spero, M., Du, M., Ahn, M., Yip, M. C., Zhang, M., Ding, M., Heo, M., Srirama, M., Sharma, M., Kim, M., Kanazawa, N., Hansen, N., Heess, N., Joshi, N. J., Suenderhauf, N., Liu, N., Di Palo, N., Shafiullah, N., Mees, O., Kroemer, O., Bastani, O., Sanketi, P. R., Miller, P., Yin, P., Wohlhart, P., Xu, P., Fagan, P., Mitrano, P., Sermanet, P., Abbeel, P., Sundaresan, P., Chen, Q., Vuong, Q., Rafailov, R., Tian, R., Doshi, R., Martin-Martin, R., Baijal, R., Scalise, R., Hendrix, R., Lin, R., Qian, R., Zhang, R., Mendonca, R., Shah, R., Hoque, R., Julian, R., Bustamante, S., Kirmani, S., Levine, S., Lin, S., Moore, S., Bahl, S., Dass, S., Sonawani, S., Tulsiani, S., Song, S., Xu, S., Haldar, S., Karamcheti, S., Adebola, S., Guist, S., Nasiriany, S., Schaal, S., Welker, S., Tian, S., Ramamoorthy, S., Dasari, S., Belkhale, S., Park, S., Nair, S., Mirchandani, S., Osa, T., Gupta, T., Harada, T., Matsushima, T., Xiao, T., Kollar, T., Yu, T., Ding, T., Davchev, T., Zhao, T. Z., Armstrong, T., Darrell, T., Chung, T., Jain, V., Kumar, V., Vanhoucke, V., Zhan, W., Zhou, W., Burgard, W., Chen, X., Chen, X., Wang, X., Zhu, X., Geng, X., Liu, X., Xu Liangwei, Li, X., Pang, Y., Lu, Y., Ma, Y., Kim, Y., Chebotar, Y., Zhou, Y., Zhu, Y., Wu, Y., Xu, Y., Wang, Y., Bisk, Y., Dou, Y., Cho, Y., Lee, Y., Cui, Y., Cao, Y., Wu, Y., Tang, Y., Zhu, Y., Zhang, Y., Jiang, Y., Li, Y., Li, Y., Iwasawa, Y., Matsuo, Y., Ma, Z., Xu, Z., Cui, Z., Zhang, Z., Fu, Z., Lin, Z., IEEE IEEE. 2024: 6892-6903

  View details for DOI 10.1109/ICRA57147.2024.10611477

  View details for Web of Science ID 001294576205021

• TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction Jiang, Y., Wang, C., Zhang, R., Wu, J., Fei-Fei, L. edited by Kroemer, O., Agrawal, P., Burgard, W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

  View details for Web of Science ID 001483833800080

• Cross-Episodic Curriculum for Transformer Agents Shi, L., Jiang, Y., Grigsby, J., Fan, L., Zhu, Y. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

  View details for Web of Science ID 001220600006039

• VIMA: Robot Manipulation with Multimodal Prompts Jiang, Y., Gupta, A., Zhang, Z., Wang, G., Dou, Y., Chen, Y., Li Fei-Fei, Anandkumar, A., Zhu, Y., Fan, L. edited by Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

  View details for Web of Science ID 001371932507007

• CSTNet: A Dual-Branch Convolutional Neural Network for Imaging of Reactive Flows Using Chemical Species Tomography IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Jiang, Y., Si, J., Zhang, R., Enemali, G., Zhou, B., McCann, H., Liu, C. 2023; 34 (11): 9248-9258

  Abstract

  Chemical species tomography (CST) has been widely used for in situ imaging of critical parameters, e.g., species concentration and temperature, in reactive flows. However, even with state-of-the-art computational algorithms, the method is limited due to the inherently ill-posed and rank-deficient tomographic data inversion and by high computational cost. These issues hinder its application for real-time flow diagnosis. To address them, we present here a novel convolutional neural network, namely CSTNet, for high-fidelity, rapid, and simultaneous imaging of species concentration and temperature using CST. CSTNet introduces a shared feature extractor that incorporates the CST measurements and sensor layout into the learning network. In addition, a dual-branch decoder with internal crosstalk, which automatically learns the naturally correlated distributions of species concentration and temperature, is proposed for image reconstructions. The proposed CSTNet is validated both with simulated datasets and with measured data from real flames in experiments using an industry-oriented sensor. Superior performance is found relative to previous approaches in terms of reconstruction accuracy and robustness to measurement noise. This is the first time, to the best of our knowledge, that a deep learning-based method for CST has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with a severely limited number of laser beams.

  View details for DOI 10.1109/TNNLS.2022.3157689

  View details for Web of Science ID 000777138500001

  View details for PubMedID 35324447

• MINEDOJO: Building Open-Ended Embodied Agents with Internet-Scale Knowledge Fan, L., Wang, G., Jiang, Y., Mandlekar, A., Yang, Y., Zhu, H., Tang, A., Huang, D., Zhu, Y., Anandkumar, A. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022

  View details for Web of Science ID 001215469505034