Rohith Peddi

Papers

Total Citations

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About

Rohith Peddi is a robotics researcher whose work centers on advancing robotic manipulation through novel trajectory optimization and perception techniques. His primary research areas include robotic grasping, motion planning, and 3D perception, with a particular focus on leveraging point-cloud representations to bridge the gap between sensing and control. In his most-cited work, "Grasping Trajectory Optimization with Point Clouds" (2024), Peddi introduces an innovative method that represents both robots and their task spaces as 3D point clouds, enabling more flexible and efficient grasp planning directly from depth sensor data. This approach allows robots to optimize grasping trajectories without relying on traditional geometric models, making it particularly valuable for unstructured environments. While his citation count is still growing, Peddi’s contribution is notable for its practical integration of perception and planning—a key challenge in modern robotics. His work has the potential to simplify robotic system design by eliminating the need for precomputed models, paving the way for more adaptive and sensor-driven manipulation in real-world applications. As an emerging researcher, Peddi is helping shape the future of dexterous robotic interaction.