Pohsun Feng

About

Pohsun Feng is a rising researcher at the intersection of artificial intelligence, robotics, and the Internet of Things (IoT). His work focuses on developing intelligent systems that enable seamless human–robot interaction and autonomous robotic control. Feng’s major contributions include the design of an automatic gripping learning system for robotic arms, which integrates convolutional neural networks with optimization algorithms to reduce the time and difficulty of real-world data collection—a practical breakthrough for industrial automation. He has also pioneered a transfer-learning-based gesture and pose recognition system for human–robot interaction, leveraging IoT frameworks to enhance mutual feedback between humans and machines. Additionally, Feng implemented a soft Actor–Critic controller for robotic arms, advancing reinforcement learning applications in robotics. With several papers already accumulating citations in 2024 and 2025, his work is gaining rapid recognition for its innovative blend of deep learning, optimization, and real-world deployment. Feng’s research is particularly notable for its focus on reducing data acquisition burdens while improving system efficiency, making his contributions highly relevant for students and engineers working on next-generation autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Developing an automatic gripping learning system for a robotic arm by integrating a convolutional neural network and optimization algorithms

   5 citations · 2025
2. 2
   Transfer-Learning-Based Gesture and Pose Recognition System for Human–Robot Interaction: An Internet of Things Application

   5 citations · 2024
3. 3
   Design and implementation of a soft Actor–Critic controller for a robotic arm

   4 citations · 2025

Key Collaborators

Contact & Links