Rishav Agarwal

Papers

Total Citations

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About

Rishav Agarwal is a robotics and computer vision researcher whose work bridges the gap between academic algorithms and industrial-grade reliability. His primary research areas include 6DoF pose estimation, autonomous agricultural robotics, and vision-based precision systems. Agarwal’s most impactful contribution is the introduction of the Industrial Plenoptic Dataset (IPD), a pioneering resource for the co-evaluation of cameras, HDR, and algorithms aimed at achieving the robustness required for mass deployment in industrial robotics. This work, published in 2024, has already garnered 7 citations, signaling its growing influence in the field. Earlier in his career, Agarwal focused on agricultural automation, developing the sTransporter—an autonomous system for collecting fresh fruit crates to streamline post-harvest handling—and a computer vision-assisted intra-row weeder for precision weed management. These projects, each with 3 citations, demonstrate his commitment to applying vision and robotics to real-world challenges in food production and sustainability. Agarwal’s trajectory from agricultural robotics to industrial-grade pose estimation highlights a versatile researcher dedicated to translating vision-based solutions into tangible, high-impact systems.