Papers
9
Total Citations
145
H-Index
7
About
Xibai Lou is a robotics researcher whose work centers on robotic manipulation, object grasping, and intelligent perception in complex, real-world environments. His research addresses some of the most persistent challenges in the field: enabling robots to reliably grasp novel target objects in cluttered, constrained, and partially occluded scenes without extensive prior knowledge of the environment. Lou's most cited contribution, "Collision-Aware Target-Driven Object Grasping in Constrained Environments" (2021, 36 citations), tackles the critical problem of 6-DoF grasp planning around obstacles such as walls, bins, and shelves. His work on attribute-based grasping demonstrates a forward-thinking approach to data-efficient robot learning, allowing systems to adapt rapidly to unseen objects using transferable object attributes — a line of research accumulating nearly 40 citations across multiple papers. Notably, his early work on slide-to-wall grasping reframed environmental structures not as obstacles but as functional grasping aids, a creative perspective reflected across several publications. Lou further extends his impact through graph neural network-based methods for reasoning about spatial object relationships and adversarial rearrangement tasks. Collectively, his body of work, spanning over 140 citations, represents meaningful advances in making robotic grasping systems more adaptive, context-aware, and deployable in unstructured real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Collision-Aware Target-Driven Object Grasping in Constrained Environments36 citations · 2021
- 2Attribute-Based Robotic Grasping With Data-Efficient Adaptation23 citations · 2024
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- 4Attribute-Based Robotic Grasping with One-Grasp Adaptation16 citations · 2021
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- 6Knowledge Induced Deep Q-Network for a Slide-to-Wall Object Grasping.15 citations · 2019
- 7IOSG: Image-Driven Object Searching and Grasping11 citations · 2023
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