Pruthvik S Kashyap

About

Pruthvik S Kashyap is a rising researcher whose work sits at the intersection of robotics and artificial intelligence, with a primary focus on multi-robot systems and deep reinforcement learning. His most notable contribution, "Goal Driven Multi-Robot Navigation in Simulated Environments with Federated Deep Reinforcement Learning," introduces a novel Federated Deep Reinforcement Learning (FDRL) framework that leverages the Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm. This work directly tackles the critical challenge of enabling multiple robots to navigate autonomously toward shared goals in complex, simulated environments. By integrating federated learning principles, Kashyap’s approach enhances both the scalability and robustness of multi-robot coordination, offering a significant step forward for real-world applications like warehouse logistics and search-and-rescue operations. While his publication record is early-career, the innovative fusion of federated learning with TD3 for multi-agent systems has already garnered attention, earning 2 citations since its 2024 release. Kashyap’s work demonstrates a clear trajectory toward solving pressing problems in decentralized robotic control, positioning him as a promising voice in the advancement of autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Goal Driven Multi-Robot Navigation in Simulated Environments with Federated Deep Reinforcement Learning

   2 citations · 2024

Key Collaborators

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