I'm Bharath Vedantha Desikan, a robotics and machine learning researcher at Arizona State University's DREAMS Lab, where I develop algorithms that enable autonomous platforms to efficiently explore and reconstruct spatial environments.
My work sits at the intersection of probabilistic modeling, information theory, and robotic decision-making. I build systems that reason about uncertainty to make smarter sampling decisions, whether that means mapping aquatic temperature fields with autonomous surface vehicles or reconstructing 3D objects with aerial drones.
I focus on turning principled mathematical frameworks into real, deployable robotic systems -- from Gaussian Process regression and information-theoretic planning to 3D Gaussian Splatting for active reconstruction.
Information-theoretic path planning with Gaussian Processes under positional uncertainty. Seven distinct planners compared across stationary and nonstationary kernels.
Active 3D Gaussian Splatting with next-best-view planning. Autonomous drones that reconstruct objects in real-time using depth-variance guided exploration.
Full ROS 2 / PX4 simulation pipelines with Docker orchestration, automated trial management, and statistical analysis frameworks.
Loading paper preview...
GP-based adaptive sampling with 7 distinct planners under positional uncertainty. Autonomous surface vehicles reconstruct aquatic temperature fields using information-theoretic planning with stationary and nonstationary Gibbs kernels.
Autonomous object reconstruction using depth-variance guided next-best-view planning with 3D Gaussian Splatting. Real-time incremental reconstruction with live splat optimization and voxel-based coverage tracking.