Education

• University College London, Robotics and AI (MEng), London, UK Sep 2023 - Jun 2027 expected; integrated undergraduate and master's programme; average score 79%.

Research Interests

• Vision-language-action models for robotics.
• Large language models and vision-language models.
• Multimodal learning and retrieval.

Selected Publications

• SignVLA: real-time sign language-guided robotic manipulation via Attention LSTM and VLA models. ICAC 2026, accepted.
• An Event-Triggered Robust Reinforcement Learning Scheme for Nonlinear Systems with Unknown Disturbances. CCC 2026, accepted.
• Reinforcement learning based optimal control: a survey of ADP for manipulators and wheeled mobile robots. Advanced Mechatronics, 2026, 1(1), 1.
• Zero-shot Decomposed Retrieval Enhancement for Visually Rich Document via Broad Search and Deep Reasoning.
• Post-Training for Reasoning LLMs with Reinforcement Learning: A Stability-Efficiency Perspective. ICAC 2026, accepted.

Research Experience

• Vision-Language-Action Model Control by Sign Language Deployed NVIDIA GR00T on a robotic manipulator, built a MediaPipe Hands gesture feature pipeline, and connected sign language understanding to VLA model commands.
• Reinforcement Learning for Nonlinear Control Systems Combined event-triggered mechanisms, extended state observers, and adaptive dynamic programming to reduce redundant updates while preserving control performance.
• Visual RAG Systems Designed a zero-shot multimodal query decomposition method for visually rich document retrieval, improving Recall@1 in experiments.

Projects

• BabyBench Infant Self-Touch RL: built a Mujoco-based self-touch learning environment and trained early exploration behavior with PPO.
• ROS-based Human-Following Agricultural Robot Platform: developed an agricultural robot vehicle platform on ROS capable of following human movement for field assistance.
• Emotion-Aware AI Intelligent Photo Album: fine-tuned dialogue and expression-recognition components with Qwen, YOLO, ResNet50, and Gradio.
• 7-DOF Robotic Arm Simulation: a self-recorded course on MATLAB / Simulink-based 7-DOF robotic arm simulation, covering kinematics, dynamics, control-system design, and modelling practice.

Skills

Python, PyTorch, C, C++, Matlab, Simulink, ROS, ROS2, Linux, STM32 HAL, Solidworks, Fusion 360, Docker, HTML, JavaScript, React, PostgreSQL.