Bingnan Huo 🤖
Bingnan Huo 霍秉楠

Masters Student

About Me

Hi! I am Bingnan Huo (霍秉楠), a masters student in Data Science at Brown University. I am a member of the Intelligent Robotics Lab, led by Prof. George Konidaris, where I conduct research on reinforcement learning and robotics. My current work focuses on option generalization under the Hierarchical Reinforcement Learning framework, aiming to develop reusable skills for robotic applications.

I have contributed to multiple research projects, including a paper submitted to ICLR 2025 on learning transferable subgoals, and I’m currently leading a project on applying these concepts to robotic motion planning using the Franka Emika Panda robot. Previously, I worked on medical image processing using computer vision and machine learning at Bucknell University, where I developed a system for diagnosing Facial Nerve Paralysis with over 80% accuracy.

My research interests lie at the intersection of artificial intelligence, reinforcement learning, and robotics, with a particular focus on developing AI systems that can learn and adapt to solve complex real-world tasks. I am passionate about bridging the gap between theoretical advances in AI and practical applications in robotics.

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Interests
  • Artificial Intelligence
  • Reinforcement Learning
  • Robotics
  • Psychology & Sociology
Education
  • Sc.M. Data Science

    Brown University

  • B.S. Statistics

    Bucknell University

🤖 Research Overview

I am a masters student in Data Science at Brown University, where I am part of the Intelligent Robotics Lab led by Prof. George Konidaris. My research focuses on developing intelligent robotic systems that can learn and adapt to solve complex real-world tasks.

Currently, I am working on:

  • Option generalization under Hierarchical Reinforcement Learning framework for learning reusable robotic skills
  • Applying transferable subgoals with motion planning for skill generalization across tasks using the Franka Emika Panda robot
  • Developing novel approaches for learning transferable subgoals as option termination classifiers (ICLR 2025 submission)

Previously at Bucknell University, I worked on medical image processing using computer vision and machine learning, developing a system for diagnosing Facial Nerve Paralysis with over 80% accuracy.

I am actively seeking collaborations in reinforcement learning, robotics, and AI. Feel free to reach out! 📫

Featured Publications

Recent research work in reinforcement learning, robotics, and machine learning.

All Publications

(2025). Learning Transferable Sub-goals For Robotic Skills. RSS.
(2024). Learning Transferable Sub-goals by Hypothesizing Generalizing Features. ICLR.
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