My research focuses on intelligent robotics and artificial intelligence, with a particular emphasis on enhancing autonomy, decision-making, and human-machine interaction. I am currently exploring the following areas:

• Multi-Task Decision-Making with Chain-of-Thought for Improved Autonomy

I investigate how integrating chain-of-thought processes into multi-task decision-making can enhance the autonomy of robotic systems. By simulating human-like reasoning, robots can better handle complex tasks in dynamic environments, making more informed and independent decisions across multiple scenarios.

• LLM-Assisted Human-Like Decision-Making and Explainability

I am exploring the use of Large Language Models (LLMs) to assist robots in making human-like decisions with enhanced explainability. By leveraging LLMs, robots can generate natural language explanations for their actions, improving transparency and fostering trust in human-robot interactions. This approach aims to bridge the gap between AI decision-making processes and human understanding.

• Generative Robot World Models with Monte Carlo Exploration

My research also focuses on constructing generative world models that include robot actions and environmental dynamics. By employing Monte Carlo methods for exploration and evaluation, I aim to predict and assess possible future actions. This enables robots to plan ahead and make optimal decisions in uncertain environments, enhancing their ability to adapt and perform complex tasks.

Please reach out to collaborate 😃