Papers
3
Total Citations
128
H-Index
3
About
Guru Koushik is a robotics researcher whose work focuses on the intersection of multi-agent systems, reinforcement learning, and real-world robot deployment. His primary research areas include multi-agent path planning, evolutionary reinforcement learning, and autonomous navigation in dynamic environments. Koushik's most significant contribution is the development of MAPPER (Multi-Agent Path Planning with Evolutionary Reinforcement Learning), a decentralized approach that enables fleets of robots to navigate complex, mixed dynamic environments. This method, detailed in his highly cited 2020 paper (113 citations), addresses the critical industrial challenge of deploying large-scale robot fleets in real-world applications by learning robust navigation policies under partial observability. The impact of this work is evident in its citation count, reflecting its relevance to both academic research and practical robotics. Additionally, Koushik has explored applied robotics through projects like an Arduino-based plastic identification and picking robot, demonstrating his versatility in bridging theoretical algorithms with tangible hardware solutions. His research continues to advance the capabilities of autonomous systems in shared spaces.
Research Focus
Key Achievements
Top Papers
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- 3Arduino-Based Plastic İdentification and Picking Robot3 citations · 2020