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I am a PhD student in Mechanical Engineering at Biomimetics & Dexterous Manipulation Laboratory, advised by Prof. Mark Cutkosky and Prof. Oussama Khatib. I was a MSc student in Stanford Vision and Learning Lab, working with Prof. Jiajun Wu and Prof. Fei-Fei Li. I previously received my dual B.S. in Mechanical Engineering from Shanghai Jiao Tong University and Purdue University, where I was fortunate to be advised by Karthik Ramani on Human-Computer Interaction. I support diversity, equity, and inclusion. If you would like to have a chat with me regrading research, career plans or anything, feel free to reach out! I would be happy to support people from underrepresented groups in the STEM research community, and hope my expertise can help you. Email / Resume / Google Scholar / Twitter / GitHub |
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I'm broadly interested in artificial intelligence and robotics, including but not limited to perception, planning, control, hardware design, and human-centered AI. The goal of my research is to build agents that can achieve human-level of learning and adapt to novel and challenging scenarios by leveraging multisensory information including vision, audio, touch, etc. |
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Ananya Ipsita*, Runlin Duan*, Hao Li*, Subramanian Chidambaram, Yuanzhi Cao, Min Liu, Alexander J Quinn, Karthik Ramani Journal of Computing and Information Science in Engineering (JCISE) arXiv Using our VRFromX system, we performed a usability evaluation with 20 DUs from which 12 were novices in VR programming with a welding use case. |
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Ruohan Gao*, Yiming Dou*, Hao Li*, Tanmay Agarwal, Jeannette Bohg, Yunzhu Li, Li Fei-Fei, Jiajun Wu Computer Vision and Pattern Recognition (CVPR), 2023 dataset demo / project page / arXiv We introduce the OBJECTFOLDER BENCHMARK, a benchmark suite of 10 tasks for multisensory object-centric learning, and the OBJECTFOLDER REAL dataset, in- cluding the multisensory measurements for 100 real-world household objects. |
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Ruohan Gao*, Hao Li*, Gokul Dharan, Zhuzhu Wang, Chengshu Li, Fei Xia, Silvio Savarese, Li Fei-Fei, Jiajun Wu International Conference on Robotics and Automation (ICRA), 2023 project page / arXiv We introduce SONICVERSE, a multisensory simulation platform with integrated audio-visual simulation for training household agents that can both see and hear. We demonstrate SONICVERSE’s realism via sim-to-real transfer. |
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Hao Li*, Yizhi Zhang*, Junzhe Zhu, Shaoxiong Wang, Michelle A. Lee, Huazhe Xu, Edward Adelson, Li Fei-Fei, Ruohan Gao†, Jiajun Wu† Conference on Robot Learning (CoRL), 2022 project page / arXiv We build a robot system that can see with a camera, hear with a contact microphone, and feel with a vision-based tactile sensor, with all three sensory modalities fused with a self-attention model. |
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Ananya Ipsita, Hao Li, Runlin Duan, Yuanzhi Cao, Subramanian Chidambaram, Min Liu, Karthik Ramani Conference on Human Factors in Computing Systems (CHI), 2021 project page / arXiv / video Using our VRFromX system, users can select region(s) of interest (ROI) in scanned point cloud or sketch in mid-air using a brush tool to retrieve virtual models and then attach behavioral properties to them. |
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Reviewer for CoRL 2023 |
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Course Assistant in AA274A: Principle of Robot Autonomy, Stanford University, 2022 Course Assistant in CS231N: Deep Learning for Computer Vision, Stanford University, 2023 |
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