Hao Li / 李皓

	Fiber Bragg Grating Whiskers for Bioinspired Hydrodynamic Perception on Underwater Robots Hao Li, Tianyu Tu, Siyue Yao, Ziyang Chang, Juhyun Jung, Xiaochi Xie, Long Yin Chung, Tian-Ao Ren, Genliang Chen, Mark Cutkosky (Corresponding Authors) In submission* project page We present FBG whiskers for bioinspired hydrodynamic perception on underwater robots.
	UMI-Underwater: Learning Underwater Manipulation without Underwater Teleoperation Hao Li, Long Yin Chung, Jack Goler, Ryan Zhang, Xiaochi Xie, Huy Ha, Shuran Song, Mark Cutkosky (Equal Contribution) Robotics: Science and Systems (RSS)*, Accepted 2026 project page / code / arXiv / tech video / field demo We introduce UMI-Underwater, a system that combines autonomous self-supervised underwater data collection with zero-shot on-land-to-underwater transfer for robust grasping.
	IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction Yingke Wang, Hao Li, Yifeng Zhu, Koven Yu, Ken Goldberg, Li Fei-Fei, Jiajun Wu, Yunzhu Li, Ruohan Zhang International Conference on Robotics and Automation (ICRA), Accepted 2026 project page / paper We present IMPASTO, a robotic oil-painting system that integrates learned pixel dynamics models with model-based planning for high-fidelity reproduction of oil paintings.
	TacCap: A Wearable FBG-Based Tactile Sensor for Seamless Human-to-Robot Skill Transfer Chengyi Xing, Hao Li, Yi-Lin Wei, Tian-Ao Ren, Tianyu Tu, Yuhao Lin, Elizabeth Schumann, Wei-Shi Zheng, Mark Cutkosky (Equal Contribution) Intelligent Robots and Systems (IROS)*, 2025 project page / arXiv We present the design of FBG-based wearable tactile sensors capable of transferring tactile data collected by human hands to robotic hands.
	Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking Hao Li, Chengyi Xing, Saad Khan, Miaoya Zhong, Mark Cutkosky (Equal Contribution) Robotics and Automation Letters (RA-L)* project page / arXiv / code We present the design of underwater whisker sensors with a sim-to-real learning framework for contact tracking.
	Navigation and 3D Surface Reconstruction from Passive Whisker Sensing Michael A. Lin, Hao Li, Chengyi Xing, Mark Cutkosky project page / arXiv / video / code We present a method for using passive whiskers to gather sensory data as brushing past objects during normal robot motion.
	The ObjectFolder Benchmark: Multisensory Object-Centric Learning with Neural and Real Objects Ruohan Gao, Yiming Dou, Hao Li, Tanmay Agarwal, Jeannette Bohg, Yunzhu Li, Li Fei-Fei, Jiajun Wu (Equal Contribution) Computer Vision and Pattern Recognition (CVPR), 2023 dataset demo / project page / arXiv / video / code We introduce a benchmark suite of 10 tasks for multisensory object-centric learning, and a dataset, in- cluding the multisensory measurements for 100 real-world household objects.
	Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear Ruohan Gao, Hao Li*, Gokul Dharan, Zhuzhu Wang, Chengshu Li, Fei Xia, Silvio Savarese, Li Fei-Fei, Jiajun Wu (Equal Contribution, in alphabetical order) International Conference on Robotics and Automation (ICRA), 2023 project page / arXiv / video / code We introduce a multisensory simulation platform with integrated audio-visual simulation for training household agents that can both see and hear.
	See, Hear, Feel: Smart Sensory Fusion for Robotic Manipulation Hao Li, Yizhi Zhang, Junzhe Zhu, Shaoxiong Wang, Michelle A. Lee, Huazhe Xu, Edward Adelson, Li Fei-Fei, Ruohan Gao†, Jiajun Wu† (Equal Contribution) (†Equal Advising) Conference on Robot Learning (CoRL)*, 2022 project page / arXiv / video / code We build a robot system that can see with a camera, hear with a contact microphone, and feel with a vision-based tactile sensor.

	Fiber Bragg Grating Whiskers for Bioinspired Hydrodynamic Perception on Underwater Robots Hao Li, Tianyu Tu, Siyue Yao, Ziyang Chang, Juhyun Jung, Xiaochi Xie, Long Yin Chung, Tian-Ao Ren, Genliang Chen, Mark Cutkosky (Corresponding Authors) In submission* project page We present FBG whiskers for bioinspired hydrodynamic perception on underwater robots.
	UMI-Underwater: Learning Underwater Manipulation without Underwater Teleoperation Hao Li, Long Yin Chung, Jack Goler, Ryan Zhang, Xiaochi Xie, Huy Ha, Shuran Song, Mark Cutkosky (Equal Contribution) Robotics: Science and Systems (RSS)*, Accepted 2026 project page / code / arXiv / tech video / field demo We introduce UMI-Underwater, a system that combines autonomous self-supervised underwater data collection with zero-shot on-land-to-underwater transfer for robust grasping.
	Multimodal Sensing for Robot-Assisted Sub-Tissue Feature Detection in Physiotherapy Palpation Tian-Ao Ren, Jorge Garcia, Seongheon Hong, Jared Grinberg, Hojung Choi, Julia Di, Hao Li, Dmitry Grinberg, Mark Cutkosky Design of Medical Device Conference (DMD), 2026 arXiv We present a compact multimodal sensor that integrates high-resolution vision-based tactile imaging with a 6-axis force-torque sensor for robust subsurface feature detection and controlled robotic palpation.
	IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction Yingke Wang, Hao Li, Yifeng Zhu, Koven Yu, Ken Goldberg, Li Fei-Fei, Jiajun Wu, Yunzhu Li, Ruohan Zhang International Conference on Robotics and Automation (ICRA), Accepted 2026 project page / paper We present IMPASTO, a robotic oil-painting system that integrates learned pixel dynamics models with model-based planning for high-fidelity reproduction of oil paintings.
	TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types Yuhao Lin, Yi-Lin Wei, Haoran Liao, Mu Lin, Chengyi Xing, Hao Li, Dandan Zhang, Mark Cutkosky, Wei-Shi Zheng Conference on Robot Learning (CoRL), 2025 project page / arXiv We propose TypeTele, a type-guided dexterous teleoperation system, which enables dexterous hands to perform actions that are not constrained by human motion patterns by introducing dexterous manipulation types into the teleoperation system.
	SLIM: A Symmetric, Low-Inertia Manipulator for Constrained, Contact-Rich Spaces Rachel Thomasson, Alessandra Bernardini, Hao Li, Chengyi Xing, Amar Hajj-Ahmad, Mark Cutkosky Robotics and Automation Letters (RA-L) project page / code We introduce SLIM, a symmetric, low-inertia robotic manipulator with a bidirectional hand and integrated wrist designed for safe and effective operation in constrained, contact-rich environments.
	TacCap: A Wearable FBG-Based Tactile Sensor for Seamless Human-to-Robot Skill Transfer Chengyi Xing, Hao Li, Yi-Lin Wei, Tian-Ao Ren, Tianyu Tu, Yuhao Lin, Elizabeth Schumann, Wei-Shi Zheng, Mark Cutkosky (Equal Contribution) Intelligent Robots and Systems (IROS)*, 2025 project page / arXiv We present the design of FBG-based wearable tactile sensors capable of transferring tactile data collected by human hands to robotic hands.
	Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking Hao Li, Chengyi Xing, Saad Khan, Miaoya Zhong, Mark Cutkosky (Equal Contribution) Robotics and Automation Letters (RA-L)* project page / arXiv / code We present the design of underwater whisker sensors with a sim-to-real learning framework for contact tracking.
	Navigation and 3D Surface Reconstruction from Passive Whisker Sensing Michael A. Lin, Hao Li, Chengyi Xing, Mark Cutkosky project page / arXiv / video / code We present a method for using passive whiskers to gather sensory data as brushing past objects during normal robot motion.
	Grasp as You Say: Language-guided Dexterous Grasp Generation Yi-Lin Wei, Jian-Jian Jiang, Chengyi Xing, Xian-Tuo Tan, Xiao-Ming Wu, Hao Li, Mark Cutkosky Wei-Shi Zheng Conference on Neural Information Processing Systems (NeurIPS), 2024 project page / arXiv / code We explores a novel task DexGYS, enabling robots to perform dexterous grasping based on human commands expressed in natural language.
	The Design of a Virtual Prototyping System for Authoring Interactive VR Environments from Real World Scans Ananya Ipsita, Runlin Duan, Hao Li, Subramanian Chidambaram, Yuanzhi Cao, Min Liu, Alexander J Quinn, Karthik Ramani (Equal Contribution) 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.
	The ObjectFolder Benchmark: Multisensory Object-Centric Learning with Neural and Real Objects Ruohan Gao, Yiming Dou, Hao Li, Tanmay Agarwal, Jeannette Bohg, Yunzhu Li, Li Fei-Fei, Jiajun Wu (Equal Contribution) Computer Vision and Pattern Recognition (CVPR), 2023 dataset demo / project page / arXiv / video / code We introduce a benchmark suite of 10 tasks for multisensory object-centric learning, and a dataset, in- cluding the multisensory measurements for 100 real-world household objects.
	Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear Ruohan Gao, Hao Li*, Gokul Dharan, Zhuzhu Wang, Chengshu Li, Fei Xia, Silvio Savarese, Li Fei-Fei, Jiajun Wu (Equal Contribution, in alphabetical order) International Conference on Robotics and Automation (ICRA), 2023 project page / arXiv / video / code We introduce a multisensory simulation platform with integrated audio-visual simulation for training household agents that can both see and hear.
	See, Hear, Feel: Smart Sensory Fusion for Robotic Manipulation Hao Li, Yizhi Zhang, Junzhe Zhu, Shaoxiong Wang, Michelle A. Lee, Huazhe Xu, Edward Adelson, Li Fei-Fei, Ruohan Gao†, Jiajun Wu† (Equal Contribution) (†Equal Advising) Conference on Robot Learning (CoRL)*, 2022 project page / arXiv / video / code We build a robot system that can see with a camera, hear with a contact microphone, and feel with a vision-based tactile sensor.
	VRFromX: From Scanned Reality to Interactive Virtual Experience with Human-in-the-Loop 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 We build a system that allows users to select region(s) of interest (ROI) in scanned point cloud or sketch in mid-air to enable interactive VR experience.