Humanoid Robot Learning

I am a second-year Ph.D. student at Fudan University and Shanghai Innovation Institute, advised by Prof. Hongyang Li and Prof. Yu Qiao. My research explores how humanoids can learn versatile loco-manipulation skills from human demonstrations and multimodal experience.

News

SMASH is accepted by IEEE Transactions on Robotics (T-RO).

EgoHumanoid is fully open-sourced and accepted by RSS 2026.

We release SMASH, mastering scalable whole-body skills for humanoid ping-pong with egocentric vision.

We release EgoHumanoid, a human-to-humanoid loco-manipulation transfer framework.

WholeBodyVLA is accepted by ICLR 2026.

Selected Projects

More Publications

Generative AI

NeurIPS 2025

ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection.

IEEE TVCG 2025

SketchRefiner: Text-Guided Sketch Refinement Through Latent Diffusion Models.

ICCV 2023

SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection.

Core Contributor

Machine Learning

AAAI 2024 Oral

Navigating Real-World Partial Label Learning: Unveiling Fine-Grained Images with Attributes.

First Author
Information Fusion 2025

Recent Advances in Complementary Label Learning.

Corresponding Author
Neural Networks 2024

ComCo: Complementary Supervised Contrastive Learning for Complementary Label Learning.

First Author

Education

2024 — Present

Fudan University

Ph.D. student in Computer Science

2021 — 2024

Chinese Academy of Sciences

M.S. in Machine Learning and Optimization

2017 — 2021

Hunan University

B.S. in Statistics · Rank 1/30

Honors & Service

Selected Honors

  • National Scholarship
  • Beijing University Basketball League · Second Place