Sreejani Chatterjee
Papers
3
Total Citations
15
H-Index
3
About
Sreejani Chatterjee is an emerging robotics researcher whose work sits at the innovative intersection of computer vision and robot control, with a particular focus on visual servoing and motion planning for robotic manipulators. Her research challenges classical approaches by moving beyond end-effector-centric control, instead developing methods that govern robots through their full configuration space using visual feedback alone. In her highly regarded 2022 work on skeleton-based adaptive visual servoing (7 citations), she introduced a groundbreaking technique that extracts a robot's shape directly from depth images, enabling whole-body configuration control. Building on this, her 2023 keypoints-based method (5 citations) leverages natural robot features and geometric projections to achieve model-light control without relying on proprioceptive sensing. Her most recent 2025 contribution (3 citations) takes a bold step further, presenting an image-space motion planning framework that computes collision-free paths entirely from visual information, eliminating the need for explicit robot models. Collectively accumulating 15 citations across just three years, Chatterjee's research is carving a compelling path toward more flexible, perception-driven robot autonomy — work of significant interest to students exploring next-generation human-robot interaction and autonomous manipulation systems.
Research Focus
Key Achievements
Top Papers
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