Jialei Huang_
Ph.D. Candidate · Tsinghua University, IIIS
I build generalizable and robust embodied intelligence. My work spans the full pipeline from perception to physical action—spatial understanding, feature extraction, control grounding, semantic alignment, and precision execution—translating theoretical breakthroughs into real-world robotic systems.
Looking forward, my vision is the physical landing of general-purpose robotics: mastering the entire end-to-end system pipeline from hardware-software co-integration to high-level reasoning.
Research
My technical philosophy is "Layered Knowledge Scaffolding across the Data Pyramid"—constructing a unified pipeline through five pillars:
SaTA: Spatially-anchored Tactile Awareness for Robust Dexterous Manipulation
Pillar 5 — Physical Landing & Execution
Led the end-to-end architecture anchoring tactile features to hand kinematics for millimeter-level geometric reasoning. 88.3% success rate vs. 53.3% baseline on extreme-precision tasks.
Tactile-VLA: Unlocking VLA Models' Physical Knowledge for Generalization
Pillar 4 — Semantic-Level Alignment
Directed a framework grounding internet-scale VLA models through tactile sensing for zero-shot, contact-rich manipulation. Pioneered Tactile Chain-of-Thought reasoning.
BSR: Decoupled Visuomotor Manipulation via Sim-to-Real Transfer
Pillar 3 — Control-Level Grounding
Engineered a decoupled visuomotor pipeline harnessing low-level physics control from scalable simulation data. Achieved zero-shot sim-to-real transfer.
Policy Contrastive Imitation Learning (PCIL)
Pillar 2 — Feature-Level Extraction
Solved fundamental bottlenecks in adversarial imitation learning by designing an interpretable contrastive representation space. New SOTA on DeepMind Control suite.
Generative 3D Part Assembly via Dynamic Graph Learning
Pillar 1 — Spatial Understanding
Designed a dynamic graph learning framework for 3D assembly, establishing foundational spatial and geometric reasoning.
Education
Tsinghua University, IIIS
Ph.D. Candidate in Computer Science
Advised by Prof. Yang Gao. Research focuses on advancing embodied intelligence through deep reinforcement learning, dexterous manipulation, tactile sensing, and sim-to-real transfer.
Peking University, EECS
B.Sc. in Computer Science
Advised by Prof. Hao Dong. Developed rigorous foundations in machine learning and computer vision.