Raghav Anand

Papers

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About

Raghav Anand is a pioneering researcher at the intersection of robotics, cloud computing, and artificial intelligence, whose work is redefining how robots learn and operate in real-world environments. His primary research areas include fog robotics, serverless computing for autonomous systems, and robot learning as a service. Anand’s most significant contribution is the concept of RILaaS (Robot Inference and Learning as a Service), introduced in his highly cited 2020 paper (35 citations), which proposes a virtualized framework for deploying deep learning models on robots. This approach enables plug-and-play skill acquisition, allowing robots to leverage off-the-shelf functional behaviors without the complexity of traditional programming. Building on this, his 2021 work on serverless multi-query motion planning (6 citations) addresses the challenge of high-dimensional motion planning in semi-structured environments like homes and warehouses. By harnessing on-demand cloud and fog computing, Anand’s method dramatically accelerates planning while reducing computational overhead. His innovative use of serverless architectures for robotics has opened new pathways for scalable, cost-effective autonomy. Anand’s research is not only advancing the field of fog robotics but also making sophisticated robotic capabilities more accessible, promising a future where robots can seamlessly adapt to dynamic tasks through cloud-powered intelligence.