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Chaoqi Liu,
Haonan Chen,
Sigmund H. Høeg*,
Shaoxiong Yao*,
Yunzhu Li,
Kris Hauser,
Yilun Du,
Flexible Multitask Learning with Factorized Diffusion Policy
2025,
[webpage],
[paper]
Spotlight at RSS 2025 2nd Workshop on Semantic Reasoning and Goal Understanding in Robotics [Link]
Factorized Diffusion Policy (FDP) is a modular robot policy framework that composes specialized
diffusion components through score aggregation, enabling scalable multitask learning, interpretable
behavior decomposition, and efficient task adaptation.
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Sigmund H. Høeg,
Aksel Vaaler,
Chaoqi Liu,
Olav Egeland,
Yilun Du,
Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
2025,
[webpage]
Hybrid Diffusion Policy (HDP) is a long-horizon robot planning framework that composes discrete
high-level plans and continuous low-level trajectories through a unified diffusion process, enabling
robust multimodal behavior generation, improved decision-making over long horizons, and flexible
conditioning for complex tasks.
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Lirui Wang,
Kevin Zhao*,
Chaoqi Liu*,
Xinlei Chen
Learning Real-World Action-Video Dynamics with Heterogeneous Masked
Autoregression
2025,
[webpage],
[arXiv]
Heterogeneous Masked Autoregression (HMA) is a real-time robotic video simulation for high-fidelity
and controllable interactions,
leveraging masked autoregressive models and heterogeneous training.
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Chaoqi Liu,
Yunzhu Li,
Kris Hauser
Localized Graph-Based Neural Dynamics Models for Terrain Manipulation
2025,
[webpage],
[arXiv]
A scalable and efficient graph-based neural terrain dynamics (gbnd) by introducing fine-grained
adaptive and automatic particle selection. The proposed framework is both orders of magnitude
efficient and more accurate than previous gbnd.
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Ramkumar Natarajan,
Chaoqi Liu,
Howie Choset,
Maxim Likhachev
Implicit Graph Search for Planning on Graphs of Convex Sets
RSS 2024,
[webpage],
[paper],
[arXiv]
A scalable and efficient way to plan on Graphs of Convex Sets (GCS) with stronger theoretical
properties. We plan on GCS using a previously developed hybrid search-optimization framework called
INSAT.
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Misc.
I have consistently maintained a perfect 4.0 GPA, excelling with top grades of A/A+ in all of my
Computer Science, Mathematics, and Statistics courses. A complete list can be found here.
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