Ronit R. Jorvekar
Papers
2
Total Citations
34
H-Index
2
About
Ronit R. Jorvekar is a researcher at the forefront of human-aware robot navigation, with a focus on integrating graph neural networks (GNNs) into autonomous systems. Their work addresses a critical challenge in robotics: enabling machines to navigate crowded, dynamic environments while respecting social norms and human comfort. Jorvekar’s most-cited paper, "A graph neural network to model disruption in human-aware robot navigation" (2021, 28 citations), introduces a novel framework that uses GNNs to predict and minimize the disruption a robot causes to human activities. This contribution is pivotal for developing socially compliant robots in settings like hospitals, airports, and public spaces. Their earlier work, "Graph Neural Networks for Human-Aware Social Navigation" (2020, 6 citations), laid the groundwork by modeling human-robot interactions as relational graphs. Jorvekar’s research bridges the gap between theoretical graph learning and practical robotics, offering scalable solutions for real-world navigation. By quantifying social disruption and embedding it into learning algorithms, they have advanced the field of social robotics, making autonomous systems more intuitive and acceptable to humans. Their work continues to inspire new directions in human-robot interaction and safe, adaptive navigation.
Research Focus
Key Achievements
Top Papers
- 1A graph neural network to model disruption in human-aware robot navigation28 citations · 2021
- 2Graph Neural Networks for Human-Aware Social Navigation6 citations · 2020