Christopher Xie
Papers
7
Total Citations
224
H-Index
6
About
Christopher Xie is a robotics and computer vision researcher whose work centers on enabling robots to perceive and interact with previously unseen objects in unstructured environments. He is best known for his pioneering contributions to **unseen object instance segmentation**, a challenging problem that requires robots to identify and delineate novel objects without prior exposure to them during training. His landmark 2021 paper, "Unseen Object Instance Segmentation for Robotic Environments," has garnered 119 citations and stands as a foundational reference in the field, with related earlier work accumulating dozens of additional citations across multiple publications. Xie's research consistently bridges the gap between synthetic training data and real-world robotic deployment, leveraging RGB-D sensing and metric learning to develop models that generalize effectively to cluttered tabletop scenes. His 2020 work on RGB-D feature embeddings introduced a metric learning framework trained purely on synthetic data, demonstrating strong sim-to-real transfer. He has also explored graph neural networks for refining instance masks in highly cluttered scenes and applied amodal 3D reconstruction techniques to improve robotic manipulation. Earlier work in model-based reinforcement learning with parametrized physical models reflects his broad interest in sample-efficient robot learning. Collectively, his research addresses a critical bottleneck in deploying autonomous robots across diverse, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Unseen Object Instance Segmentation for Robotic Environments119 citations · 2021
- 2Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation45 citations · 2020
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- 4Unseen Object Instance Segmentation for Robotic Environments10 citations · 2020
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