Yufei Ding | 丁昱菲
Research
SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation
Zekun Qi*, Wenyao Zhang*, Yufei Ding*, Runpei Dong, Xinqiang Yu, Jingwen Li, Lingyun Xu, Baoyu Li, Xialin He, Guofan Fan, Jiazhao Zhang, Jiawei He, Jiayuan Gu, Xin Jin, Kaisheng Ma, Zhizheng Zhang, He Wang, Li Yi
NeurIPS 2025, Spotlight
Paper / Project Page / Code / Huggingface
We introduce the concept of semantic orientation and propose a generalizable model for language-grounded spatial reasoning and robot manipulation.
Open6DOR: Benchmarking Open-instruction 6-DoF Object Rearrangement and A VLM-based Approach
Yufei Ding*, Haoran Geng*, Chaoyi Xu, Xiaomeng Fang, Jiazhao Zhang, Songlin Wei, Qiyu Dai, Zhizheng Zhang, He Wang†
Paper / Project Page / Code / Video / Bibtex
IROS 2024, Oral Presentation
CVPR 2024 @ VLADA, Oral Presentation
ICRA 2024 @ 3D Manipulation, Spotlight
We present Open6DOR, a challenging and comprehensive benchmark for open-instruction 6-DoF object rearrangement tasks. Following this, we propose a zero-shot method, Open6DORGPT, which achieves SOTA performance and proves effective in demanding simulation environments and real-world scenarios.
RoboVerse Team
Paper / Project / Code / Bibtex
RSS 2025
We propose RoboVerse, a comprehensive framework for advancing robotics through a simulation platform, synthetic dataset, and unified benchmarks. Its MetaSim infrastructure abstracts diverse simulators into a universal interface, ensuring interoperability and extensibility. RoboVerse improves sim-to-real transfer and enables consistent evaluation for imitation and reinforcement learning, addressing key challenges in scaling robotic data and benchmarking.
DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes
Jialiang Zhang*, Haoran Liu*, Danshi Li*, Xinqiang Yu*, Haoran Geng, Yufei Ding, Jiayi Chen, He Wang†
CoRL 2024
We synthesized a large-scale dexterous grasping dataset in cluttered scenes and designed a generative framework to learn grasping in the real world.
Simulately: Handy information and resources for physics simulators for robot learning research.
Haoran Geng, Yuyang Li, Yuzhe Qin, Ran Gong, Wensi Ai, Yuanpei Chen, Puhao Li, Junfeng Ni, Zhou Xian, Songlin Wei, Yang You, Yufei Ding, Jialiang Zhang
Website / Github
Open-source Project
Selected into CMU 16-831t
Simulately is a project where we gather useful information of robotics & physics simulators for cutting-edge robot learning research.
Selected Awards