
Field Robotics and Learning Group

Our group is dedicated to advancing autonomous systems across field research in three core areas: Reliable Sensing and Perception, Intelligent Planning and Decision Making, and Advanced Robust Control. Our mission is to develop resilient technologies that enhance system reliability by integrating sophisticated sensing, perception, and adaptive control frameworks. By leveraging the latest advances in machine learning, optimization, and control theory, we address real-world challenges, enabling autonomous platforms to navigate and operate effectively in complex, dynamic environments.
news
selected publications
- T-ASE
An End-to-End Deep Reinforcement Learning Based Modular Task Allocation Framework for Autonomous Mobile SystemsIEEE Transactions on Automation Science and Engineering, 2024 - AAAS
Adaptive Unsupervised Learning-Based 3D Spatiotemporal Filter for Event-Driven CamerasResearch, 2024 - IROS
ShorelineNet: An Efficient Deep Learning Approach for Shoreline Semantic Segmentation for Unmanned Surface Vehicles2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021 - T-ASE
Anisotropic GPMP2: A Fast Continuous-Time Gaussian Processes Based Motion Planner for Unmanned Surface Vehicles in Environments With Ocean CurrentsIEEE Transactions on Automation Science and Engineering, 2022 - Applied Ocean
Image segmentation in marine environments using convolutional LSTM for temporal contextApplied Ocean Research, 2023 - Oceans Engineering
Reliable LiDAR-based ship detection and tracking for Autonomous Surface Vehicles in busy maritime environmentsOcean Engineering, 2024