Shashank Kapoor
Papers
2
Total Citations
5
H-Index
2
About
Shashank Kapoor’s research lies at the compelling intersection of human-machine interaction and autonomous robotics, where he investigates both the psychological foundations of trust and the technical performance of intelligent systems. His work on trust in human-machine interaction, published in 2025, has already garnered 3 citations, establishing a framework for understanding how principles of dependability and cooperation—long studied in human relationships—can be applied to build reliable partnerships between people and machines. In parallel, Kapoor has made significant contributions to robotics through his 2024 study comparing Proximal Policy Optimization (PPO) and Generative Adversarial Imitation Learning (GAIL) in complex search-and-retrieve tasks. This work, with 2 citations, addresses a critical gap in understanding how reinforcement learning models collaborate during multi-agent operations, offering practical insights for deploying autonomous systems in real-world scenarios. By bridging social science and artificial intelligence, Kapoor’s research provides a roadmap for designing robots that are not only technically proficient but also worthy of human trust—a dual contribution that positions him as a thoughtful voice in the future of human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 2