Papers
39
Total Citations
1,129
H-Index
18
About
Lujia Wang is a prominent robotics and artificial intelligence researcher whose work spans cloud robotics, federated learning, autonomous navigation, and 3D perception. She is perhaps best known for pioneering the intersection of federated learning and robotics, most notably through her groundbreaking work on Lifelong Federated Reinforcement Learning (2019, 266 citations), which introduced a novel architecture enabling robots to share, transfer, and build upon experiential knowledge within cloud robotic systems. This foundational contribution has significantly influenced how multi-robot systems approach continuous learning and adaptation in dynamic environments. Wang further extended this paradigm with Federated Imitation Learning (2020, 87 citations), enabling robots with heterogeneous sensors to collaboratively learn behaviors, mirroring human observational learning. Her contributions to resource allocation in cloud robotics (2016, 69 citations) and multi-sensor data retrieval (2015, 66 citations) helped establish core infrastructure principles for the field. More recently, she has advanced autonomous perception through stereo 3D object detection (2021, 83 citations) and released influential datasets, FusionPortable and FusionPortableV2, benchmarking SLAM across diverse robotic platforms. With over 700 cumulative citations across her most recognized works, Wang's research continues to shape the future of intelligent, collaborative robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection83 citations · 2021
- 4
- 5Real-Time Multisensor Data Retrieval for Cloud Robotic Systems66 citations · 2015
- 6
- 7
- 8A Dynamic Points Removal Benchmark in Point Cloud Maps32 citations · 2023
- 9R-PCC: A Baseline for Range Image-based Point Cloud Compression31 citations · 2022
- 10