Tianshu Wu

I am a Ph.D. student at Peking University, advised by Prof. Hao Dong. Before this, I earned my Bachelor's degree from the Harbin Institute of Technology.

My current research is centered on humanoid robot learning, with a specific focus on loco-manipulation and humanoid-object interaction. I am dedicated to developing general, generalizable, and scalable learning methods, aiming to empower humanoid robots to execute a wide variety of real-world tasks with human-like capability.

Email / GitHub / Scholar

Publications

	🍬SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework Tianshu Wu, Xiangqi Kong, Yue Chen, Qize Yu, Hang Ye, Jia Li, Yizhou Wang, Hao Dong Advances in Neural Information Processing Systems (NeurIPS)*, 2026 paper / code / project We present SUGAR, a data-driven framework that converts diverse human videos into deployable humanoid loco-manipulation skills in a scalable and generalizable manner.
	RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies Tianxing Chen§, Yue Chen§†, Zixuan Li, Junyuan Tang, Kailun Su, Haoran Lu, Weijie Wan, Baijun Chen, Songling Liu, Haowen Yan, Honghao Su, Zhiyang Dou, Kaixuan Wang, Dandan Zhang, Yunze Liu, Yan Qin, Qiwei Liang, Qiwei Wu, Zijian Lin, Wenwei Lin, Yuran Wang, Minghua He, Tianshu Wu, Ruihai Wu, Jingquan Zhou, Kai-Chong Lei, Haibao Yu, Yuanfeng Ji, Weiyang Jin, Guanyu Lin, Xiaofan Li, Qi Xiong, Renjing Xu, Zhongyu Li, Wenhao Chai, Enze Xie, Ziwei Wang, Yao Mu, Hao Dong, Wojciech Matusik, Mingyu Ding†, Wenbo Ding†, Ping Luo†, Masayoshi Tomizuka† Arxiv*, 2026 paper / code / project We present RoboDojo, a unified sim-and-real benchmark with 42 simulation tasks and 18 real-world tasks for comprehensive evaluation of generalist robot manipulation policies.
	Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approach Tianshu Wu, Jiyao Zhang, Shiqian Liang, Zhengxiao Han, Hao Dong International Conference on Robotics and Automation (ICRA)*, 2025 paper / code / project We present FEEPE, a foundation feature-driven online end-effector pose estimation method that is marker-free and learning-free, enabling accurate and real-time pose estimation for end-effector in complex environments.
	OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints Mingjie Pan, Jiyao Zhang, Tianshu Wu, Yinghao Zhao, Wenlong Gao, Hao Dong Conference on Computer Vision and Pattern Recognition (CVPR), 2025, Highlight paper / project Bridging high-level reasoning and precise 3D manipulation, OmniManip uses object-centric representations to translate VLM outputs into actionable 3D constraints. A dual-loop system combines VLM-guided planning with 6D pose tracking for execution, achieving generalization in diverse robotic tasks with a zero-training manner.
	CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation Yuxing Long, Jiyao Zhang, Mingjie Pan, Tianshu Wu, Taewhan Kim, Hao Dong Conference on Computer Vision and Pattern Recognition (CVPR), 2025, Highlight paper / code / project The first benchmark for manual-based appliance manipulation.

	🍬SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework Tianshu Wu, Xiangqi Kong, Yue Chen, Qize Yu, Hang Ye, Jia Li, Yizhou Wang, Hao Dong Advances in Neural Information Processing Systems (NeurIPS)*, 2026 paper / code / project We present SUGAR, a data-driven framework that converts diverse human videos into deployable humanoid loco-manipulation skills in a scalable and generalizable manner.
	RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies Tianxing Chen§, Yue Chen§†, Zixuan Li, Junyuan Tang, Kailun Su, Haoran Lu, Weijie Wan, Baijun Chen, Songling Liu, Haowen Yan, Honghao Su, Zhiyang Dou, Kaixuan Wang, Dandan Zhang, Yunze Liu, Yan Qin, Qiwei Liang, Qiwei Wu, Zijian Lin, Wenwei Lin, Yuran Wang, Minghua He, Tianshu Wu, Ruihai Wu, Jingquan Zhou, Kai-Chong Lei, Haibao Yu, Yuanfeng Ji, Weiyang Jin, Guanyu Lin, Xiaofan Li, Qi Xiong, Renjing Xu, Zhongyu Li, Wenhao Chai, Enze Xie, Ziwei Wang, Yao Mu, Hao Dong, Wojciech Matusik, Mingyu Ding†, Wenbo Ding†, Ping Luo†, Masayoshi Tomizuka† Arxiv*, 2026 paper / code / project We present RoboDojo, a unified sim-and-real benchmark with 42 simulation tasks and 18 real-world tasks for comprehensive evaluation of generalist robot manipulation policies.
	Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approach Tianshu Wu, Jiyao Zhang, Shiqian Liang, Zhengxiao Han, Hao Dong International Conference on Robotics and Automation (ICRA)*, 2025 paper / code / project We present FEEPE, a foundation feature-driven online end-effector pose estimation method that is marker-free and learning-free, enabling accurate and real-time pose estimation for end-effector in complex environments.
	OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints Mingjie Pan, Jiyao Zhang, Tianshu Wu, Yinghao Zhao, Wenlong Gao, Hao Dong Conference on Computer Vision and Pattern Recognition (CVPR), 2025, Highlight paper / project Bridging high-level reasoning and precise 3D manipulation, OmniManip uses object-centric representations to translate VLM outputs into actionable 3D constraints. A dual-loop system combines VLM-guided planning with 6D pose tracking for execution, achieving generalization in diverse robotic tasks with a zero-training manner.
	CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation Yuxing Long, Jiyao Zhang, Mingjie Pan, Tianshu Wu, Taewhan Kim, Hao Dong Conference on Computer Vision and Pattern Recognition (CVPR), 2025, Highlight paper / code / project The first benchmark for manual-based appliance manipulation.