Dadhichi Shukla
Papers
10
Total Citations
132
H-Index
6
About
Dadhichi Shukla is a leading researcher in human-robot interaction, with a focus on enabling intuitive, non-verbal communication between humans and collaborative robots. His work centers on deictic gestures, social gaze, and hand gesture recognition, aiming to make robots more responsive and adaptive partners in shared tasks. Shukla’s most influential paper, “Probabilistic Detection of Pointing Directions for Human-Robot Interaction” (34 citations), pioneered probabilistic methods for interpreting pointing gestures, a fundamental cue in human collaboration. He also introduced the Innsbruck Multi-View Hand Gesture (IMHG) dataset (22 citations), a key resource for gesture recognition research. His studies on social gaze in collaborative assembly (32 citations) and semantic learning of gestural instructions (20 citations) demonstrate how robots can infer human intent and adjust behavior in real time. Shukla’s notable achievement includes developing a proactive, incremental learning framework that allows robots to continuously improve gesture-action associations, enhancing efficiency in domestic and industrial settings. His work has laid the groundwork for more natural, human-aware robotic systems, with cumulative citations exceeding 130, reflecting its impact on the field.
Research Focus
Key Achievements
Top Papers
- 1Probabilistic Detection of Pointing Directions for Human-Robot Interaction34 citations · 2015
- 2The Effects of Social Gaze in Human-Robot Collaborative Assembly32 citations · 2015
- 3
- 4Learning Semantics of Gestural Instructions for Human-Robot Collaboration20 citations · 2018
- 5Negotiating Instruction Strategies during Robot Action Demonstration6 citations · 2015
- 6It Gets Worse Before it Gets Better6 citations · 2017
- 7
- 8
- 9General Object Tip Detection and Pose Estimation for Robot Manipulation2 citations · 2015
- 10Visual task outcome verification using deep learning2 citations · 2017