Papers
2
Total Citations
32
H-Index
2
About
Souvik Barat is a leading researcher at the intersection of artificial intelligence, autonomous systems, and cyber-physical control. His work primarily focuses on bridging the gap between advanced machine learning techniques and real-world operational reliability. Barat’s most significant contribution is in the application of Reinforcement Learning (RL) to business-critical systems, as demonstrated in his highly cited 2019 paper on "Actor Based Simulation for Closed Loop Control of Supply Chain using Reinforcement Learning" (30 citations). This work pioneered the use of RL for managing complex supply chain operations, moving beyond traditional game and robotics domains to address practical industrial challenges. More recently, Barat has advanced autonomous space robotics, developing integrated slip detection and grip force control systems for in-situ resource utilization on the Moon and Mars. His 2024 paper on this topic (2 citations) showcases his commitment to creating fail-safe autonomous assembly processes. By combining simulation-based training with robust physical control, Barat is helping to make autonomous systems more dependable for critical infrastructure and extraterrestrial construction, marking him as a key innovator in applied AI and robotics.
Research Focus
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Top Papers
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