Arvind Kruthiventy

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About

Arvind Kruthiventy is a leading researcher at the intersection of robotics, geometric deep learning, and control theory, with a particular focus on SE(3)-equivariant methods for robot manipulation and motion planning. His most cited work, the tutorial survey “SE(3)-equivariant Robot Learning and Control,” has already garnered 6 citations since its 2025 publication, establishing a foundational resource for the emerging field of symmetry-aware robot learning. Kruthiventy’s major contributions lie in developing theoretical frameworks that leverage the inherent symmetries of physical space—specifically rotations and translations—to improve sample efficiency and generalization in robot learning algorithms. By formalizing how neural networks and control policies can respect SE(3) group structure, his research enables robots to transfer skills across different poses and environments without retraining. This work has direct implications for dexterous manipulation, autonomous navigation, and human-robot interaction. Kruthiventy is also recognized for bridging the gap between pure geometric algebra and practical robotics, making advanced equivariant methods accessible to practitioners. His clear, tutorial-style exposition in the survey paper has made him a go-to voice for students and engineers entering this rapidly growing area.