Welcome
The Lu’s Navigation and Autonomous Robotics (Lunar) Lab at Georgia Tech studies problems related to perception and navigation for robots and autonomous systems in ground, air, and space applications. The lab’s primary goal is to enable robotic systems to operate autonomously in highly unstructured environments under harsh sensing conditions for tasks including search and rescue, daily housework, scientific exploration and discovery. The main research areas of the lab include computer vision, machine learning, deep learning, estimation, and probabilistic inference.
Recent Projects
GaussianFormer3D: Multi-Modal Gaussian-based Semantic Occupancy Prediction with 3D Deformable Attention
Lingjun Zhao, Sizhe Wei, James Hays, Lu Gan
GaussianFormer3D is a LiDAR-camera fusion-based semantic occupancy prediction framework based on a compact and continuous object-centric scene representation.
Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
Fengze Xie*, Sizhe Wei*, Yue Song, Yisong Yue, Lu Gan
L4DC, 2025
MS-HGNN is a general and efficient deep learning model for robotic dynamics learning, that integrates both robotic kinematic structures and morphological symmetries.
MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception
Daniel Butterfield, Sandilya Sai Garimella, Nai-Jen Cheng, Lu Gan
ICRA, 2025
MI-HGNN is a structured deep learning model that incorporates robot morphological constraints; designed as a learning-based contact estimator for legged robots.
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Latest News
- 04/01/2025: Daniel presents his paper at the ECE Research Rally.
- 03/25/2025: We are excited to welcome Ziwon, Jinkua, Kausar and Cam to our group!
- 03/20/2025: Lu will serve as Area Chair for CoRL 2025, and International Program Committee member for ICRA’25 Doctoral Consortium.
- 03/01/2025: Lu is co-organizing the Foundation Models and Neuro-Symbolic AI for Robotics workshop at ICRA 2025.
- 02/28/2025: MS-HGNN was accepted to L4DC 2025. See you in Ann Arbor!
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