Shida Nie
Papers
2
Total Citations
8
H-Index
2
About
Shida Nie’s research bridges the frontiers of robotics, control theory, and 3D computer vision, with a focus on enabling intelligent systems to learn and perceive more effectively. A key contribution is in inverse optimal control (IOC), where Nie’s work on “Inverse Model Predictive Control” introduces a novel framework for learning the underlying cost functions that drive expert demonstrations. This approach allows autonomous systems to infer human preferences and goals directly from observed behavior, a critical step toward more intuitive and adaptable robot control. With 6 citations since its 2024 publication, this work is gaining traction in the learning-from-demonstration community. Complementing this, Nie has also contributed a comprehensive review of deep learning-based 3D object detection in indoor environments, addressing a gap in a field often dominated by outdoor autonomous driving research. This survey provides a vital resource for researchers working on indoor robotics and scene understanding. By tackling both the “how” of learning control objectives and the “what” of perceiving complex environments, Shida Nie is helping to build more capable, perceptive, and human-aligned autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Deep Learning based 3D Object Detection in Indoor Environments: A Review2 citations · 2022