Ning Gao

Ning Gao

PhD Student

Bosch Center for AI

Biography

Ning Gao is a fourth-year PhD student at Autonomous Learning Robots (ALR) at Karlsruhe Institute of Technology (KIT) supervised by Prof. Gerhard Neumann and doctoral researcher at Bosch Center for Artificial Intelligence (BCAI), Renningen, Germany. His research interests lie in the field of meta-learning, self-supervised learning and representation learning for robotic perception & manipulation.

Interests
  • Few-shot learning, meta-learning, self-supervised learning
  • Robotic vision & manipulation
  • 3D Vision
  • Scene Understanding
Education
  • PhD in Robotic Vision, 2020-now

    Karlsruhe Institute of Technology

  • MSc in Mechanical Engineering, 2019

    Karlsruhe Institute of Technology

  • BSc in Automotive Engineering, 2016

    Shanghai Tongji University

Experience

 
 
 
 
 
Karlsruhe Institute of Technology
Ph.D Candidate in Computer Science
May 2020 – May 2024 Karlsruhe, Germany
Doctoral advisor: Prof. Gerhard Neumann.
 
 
 
 
 
Bosch Center for AI
Doctoral Researcher
May 2020 – May 2024 Renningen, Germany
Meta-learning, representation learning and robotic manipulation
 
 
 
 
 
Bosch Corporate Research
Student Research Intern
March 2018 – October 2019 Renningen, Germany
Driver gaze estimation.
 
 
 
 
 
Institute of Measurement and Control Systems (MRT)
Student Research Assistant
November 2017 – May 2018 Karlsruhe, Germany
Joint tracking for pedestrians and vehicles

Recent Publications

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(2023). Enhancing Interpretable Object Abstraction via Clustering-based Slot Initialization. In BMVC23'.

PDF Cite Poster Video

(2023). Meta-Learning Regrasping Strategies for Physical-Agnostic Objects. Under review.

PDF Poster Video ICRA workshop on Scaling Robot Learning

(2023). SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects. In CoRL23'.

PDF Cite Project Poster Video

(2022). What Matters For Meta-Learning Vision Regression Tasks?. In CVPR22'.

PDF Cite Code Dataset Poster

(2022). Category-Agnostic 6D Pose Estimation with Conditional Neural Processes. In CVPR22’ WiCV workshop.

PDF Cite Poster