Aung Paing

About

Aung Paing is a robotics researcher whose work centers on autonomous navigation, coverage path planning (CPP), and intelligent cleaning systems. His most impactful contribution is a novel framework that reformulates CPP as a Travelling Salesman Problem (TSP) and solves it using Deep Reinforcement Learning (DRL), enabling efficient area coverage in large, complex environments. This work, published in 2020, has garnered 89 citations, reflecting its significance in advancing robotic autonomy. Paing is also a pioneer in developing specialized cleaning robots, notably the sTetro-D, an autonomous descending-stair cleaning robot that integrates RGB-D sensing for detection, approach, and area coverage. This system addresses a critical gap in commercial robotics—none of the existing cleaning robots target staircases. His research not only pushes the boundaries of path planning algorithms but also translates them into practical, deployable hardware solutions. By combining deep learning with real-world robotic applications, Paing is helping to create smarter, more versatile autonomous systems for domestic and industrial use.

Research Focus

Key Achievements

Top Papers

1. 1
   Coverage Path Planning for Decomposition Reconfigurable Grid-Maps Using Deep Reinforcement Learning Based Travelling Salesman Problem

   89 citations · 2020
2. 2
   sTetro-D: A deep learning based autonomous descending-stair cleaning robot

   16 citations · 2023
3. 3
   An Autonomous Descending-Stair Cleaning Robot with RGB-D based Detection, Approaching, and Area coverage Process

   2 citations · 2022

Key Collaborators

Contact & Links