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About

Chaitanya Bandi is a researcher at the forefront of human-robot interaction (HRI), specializing in computer vision and deep learning to make robotic systems more intuitive and perceptive. His work centers on enabling robots to understand human intent through skeleton-based action recognition, gaze tracking, and pose estimation. Bandi’s most cited paper, "Skeleton-based Action Recognition for Human-Robot Interaction using Self-Attention Mechanism" (2021, 21 citations), introduces a self-attention framework for motion prediction in supermarket assistance scenarios, demonstrating how robots can anticipate and respond to human actions in real-world settings. He further advances HRI with "A New Efficient Eye Gaze Tracker for Robotic Applications" (2023, 4 citations), leveraging deep learning for appearance-based gaze estimation to gauge user engagement. His recent "Action Recognition via Multi-View Perception Feature Tracking" (2025, 2 citations) integrates object detection, body and hand pose estimation, and tracking into a unified system for seamless interaction. Bandi’s contributions are pivotal in bridging the gap between human intent and robotic response, with applications ranging from assistive robotics to collaborative manufacturing. His work, though early in citation impact, lays essential groundwork for more responsive and context-aware autonomous systems.