Kwan-Woo Park

About

Kwan-Woo Park is a leading researcher in robotics and artificial intelligence, specializing in deep reinforcement learning (DRL) for complex manipulation tasks. His work addresses the critical challenge of path planning for multi-arm robotic manipulators operating in dynamic environments with both fixed and moving obstacles. Park’s most cited paper (25 citations) introduces a novel algorithm that combines the Soft Actor-Critic (SAC) method with LSTM-based position prediction, enabling real-time, collision-free motion planning in high-dimensional, continuous action spaces. This contribution is pivotal for advancing autonomous manufacturing, warehouse logistics, and human-robot collaboration. By integrating predictive modeling with DRL, Park’s research significantly improves the safety and efficiency of multi-robot systems, offering a scalable solution for industrial applications. His work has garnered attention for bridging theoretical reinforcement learning with practical robotic control, establishing him as an emerging authority in intelligent automation. Park’s achievements highlight the transformative potential of AI-driven robotics in tackling real-world operational constraints.

Research Focus

Key Achievements

Top Papers

1. 1
   Path Planning for Multi-Arm Manipulators Using Soft Actor-Critic Algorithm with Position Prediction of Moving Obstacles via LSTM

   25 citations · 2022

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