whoami

Hi, I'm Ningwei Bai.

Bio

I am Ningwei Bai, a fourth-year Robotics and AI (MEng) student at University College London. My interests sit around robotics, reinforcement learning, vision-language-action models, and post-training for reasoning large language models.

Outside research, I am also a coffee person with an SCA Intermediate Barista certificate. I like building things carefully, whether that means a robot policy, a small web tool, or a cup of coffee that actually tastes like the idea in my head.

CV Snapshot

Education

  • University College London, Robotics and AI (MEng), London, UK Sep 2023 - Jun 2027 expected; integrated undergraduate and master's programme; average score 79.5%.

Research Interests

  • Reinforcement learning for robotics and nonlinear control systems.
  • Vision-language-action models for robotic manipulation.
  • Adaptive dynamic programming, reasoning LLM post-training, and multimodal 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, accepted.
  • 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

Skills

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