Seunghyeop Nam

About

Seunghyeop Nam is a leading researcher at the frontier of autonomous mobile robotics, specializing in the integration of Simultaneous Localization and Mapping (SLAM) with Deep Reinforcement Learning (DRL). His work directly addresses the critical challenge of enabling robots to navigate safely and efficiently through dynamic, cluttered indoor environments where traditional static-map methods fail. Nam’s major contribution is the development of novel hybrid frameworks that synergize SLAM’s spatial awareness with DRL’s adaptive decision-making. His foundational paper, "SLAM-DRLnav" (2023, 7 citations), pioneered this approach by fusing the two techniques to overcome the limitations of each used in isolation. He further advanced the field with "iNAV-drlSLAM" (2023, 3 citations), which improved dynamic obstacle avoidance, and his latest work, "Multi-head Fusion-based Actor-Critic DRL" (2025, 2 citations), introduces memory contextualization for robust, end-to-end navigation in frequently reconfigured spaces. Through these innovations, Nam is shaping the next generation of intelligent, self-driving robots capable of operating reliably in the unpredictable real world.

Research Focus

Key Achievements

Top Papers

1. 1
   SLAM-DRLnav: A SLAM-Enhanced Deep Reinforcement Learning Navigation Framework for Indoor Self-driving

   7 citations · 2023
2. 2
   iNAV-drlSLAM: An Improved Indoor Self-Driving Framework for Mobile Robots using Deep Reinforcement Learning integrated with SLAM

   3 citations · 2023
3. 3
   Multi-head Fusion-based Actor-Critic Deep Reinforcement Learning with Memory Contextualisation for End-to-End Autonomous Navigation

   2 citations · 2025

Key Collaborators

Contact & Links