Yufeng Jin

I am a PhD student at PEARL Lab at TU Darmstadt, jointly advised by Prof. Georgia Chalvatzaki and Dr. Mathias Franzius (Honda Research Institute Europe).
My research focuses on object 6D pose estimation and uncertainty modeling, with broader interests in robotics perception and human-object interaction.

Before starting my PhD in December 2023, I worked as a Robotics Perception Engineer at the Honda Research Institute Europe, where I conducted applied research in robot vision and teleoperation scenarios.

I received my Master’s degree in Mechatronics and Computer Science from the Karlsruhe Institute of Technology (KIT) in 2021.
My background bridges the domains of robotics and computer vision.

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Research

SE3-PoseFlow SE(3)-PoseFlow: Estimating 6D Pose Distributions for Uncertainty-Aware Robotic Manipulation
Yufeng Jin, Niklas Funk, Vignesh Prasad, Zechu Li, Mathias Franzius, Jan Peters, Georgia Chalvatzaki
ICRA, 2026
paper / website

A probabilistic framework that leverages flow matching on the SE(3) manifold to estimate full 6D object pose distributions, enabling uncertainty-aware robotic manipulation under partial observability, occlusions, and symmetries.

Robot-DIFT Robot-DIFT: Distilling Diffusion Features for Geometrically Consistent Visuomotor Control
Yu Deng*, Yufeng Jin*, Xiaogang Jia, Jiahong Xue, Gerhard Neumann, Georgia Chalvatzaki
arXiv, 2026
paper

A method that distills geometric features from pre-trained diffusion models via Manifold Distillation into a deterministic Spatial-Semantic Feature Pyramid Network, achieving geometrically consistent visuomotor control for robot manipulation with real-time performance.

Morphologically Symmetric Reinforcement Learning for Ambidextrous Bimanual Manipulation
Zechu Li, Yufeng Jin, Daniel Ordoñez Apraez, Claudio Semini, Puze Liu, Georgia Chalvatzaki
CoRL, 2025
paper / website

A novel RL framework that explicitly leverages the inherent morphological symmetry in bimanual robotic systems to enable ambidextrous control.

6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting
Yufeng Jin, Vignesh Prasad, Snehal Jauhri, Mathias Franzius, Georgia Chalvatzaki
ICCV, 2025
paper / website

A novel model-free framework for real-time 6D object pose estimation that leverages Gaussian Splatting for fast, accurate tracking and reconstruction from RGB-D input.


Design and source code from Jon Barron's website.