Mona Gridseth
Papers
8
Total Citations
124
H-Index
6
About
Mona Gridseth is a robotics researcher whose work spans two compelling and interconnected domains: autonomous robot navigation and human-robot interaction. Her research tackles fundamental challenges in enabling robots to operate effectively both independently and alongside humans in complex, real-world environments. In the field of autonomous navigation, Gridseth has made notable contributions to visual localization and sensor-based trajectory estimation. Her 2021 work on deep learned features for long-term visual localization — earning 31 citations — demonstrated that neural networks could reliably map and localize robots across dramatic lighting changes, including complete darkness. Complementing this, her unsupervised learning framework for LiDAR features integrated deep learning with probabilistic trajectory estimation under a unified objective, advancing the frontier of mobile robot perception. On the human-robot interaction side, Gridseth has pioneered intuitive interfaces for collaborative manipulation. Her research on visual pointing gestures (30 citations) and the ViTa task specification interface (21 citations) explored how natural, nonverbal communication and image-based tools could make robot arms more accessible to non-expert users. Earlier foundational work on visual servoing further reflects her sustained commitment to bridging perception and control in practical robotic systems. Across more than 120 cumulative citations, her portfolio reflects a researcher dedicated to making robots both smarter and more human-friendly.
Research Focus
Key Achievements
Top Papers
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- 5Towards Practical Visual Servoing in Robotics10 citations · 2013
- 6On Visual Servoing to Improve Performance of Robotic Grasping9 citations · 2015
- 7Towards Direct Localization for Visual Teach and Repeat6 citations · 2019
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