Projects

Unleashing Creativity with AI and Robots

Publications

2026

[J41] Prompt-to-Product: Generative Assembly via Bimanual Manipulation
Ruixuan Liu, Philip Huang, Ava Pun, Kangle Deng, Shobhit Aggarwal, Zhenran Tang, Michelle Liu, Deva Ramanan, Jun-Yan Zhu, Jiaoyang Li and Changliu Liu
IEEE Robotics and Automation Magazine, 2026
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Abstract:

Creating assembly products demands significant manual effort and expert knowledge in 1) designing the assembly and 2) constructing the product. This paper introduces Prompt-to-Product, an automated pipeline that generates real-world assembly products from natural language prompts. Specifically, we leverage LEGO bricks as the assembly platform and automate the process of creating brick assembly structures. Given the user design requirements, Prompt-to-Product generates physically buildable brick designs, and then leverages a bimanual robotic system to construct the real assembly products, bringing user imaginations into the real world. We conduct a comprehensive user study, and the results demonstrate that Prompt-to-Product significantly lowers the barrier and reduces manual effort in creating assembly products from imaginative ideas.

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[U] CAD-Prompted SAM3: Geometry-Conditioned Instance Segmentation for Industrial Objects
Zhenran Tang, Rohan Nagabhirava and Changliu Liu
arXiv:2602.20551, 2026
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[U] BrickCraft: Visuomotor Skill Composition with Situated Manual Guidance for Long-Horizon Interlocking Brick Assembly
Jichuan Yu, Bowei Li, Zhenran Tang, Guanxing Lu, Chuxiong Hu, Ruixuan Liu and Changliu Liu
arXiv:2605.07605, 2026
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Abstract:

Autonomous robotic assembly of interlocking bricks demands seamless integration of long-horizon task reasoning, spatial grounding, and fine-grained manipulation. This paper presents BrickCraft, a compositional framework designed for long-horizon and generalizable interlocking brick assembly. BrickCraft models the assembly process using a relative formulation, where each step is anchored to a reference brick within the partial structure, thereby decomposing complex tasks into a finite set of reusable primitive skills. BrickCraft bridges the gap between high-level assembly plans and physical execution through situated manuals, which provide explicit spatial guidance for learned visuomotor skills by projecting the assembly intent onto real-time robot observations. Finally, BrickCraft employs a compositional execution pipeline that chains these spatially grounded skills to accomplish long-horizon assembly tasks. Extensive experimental validations demonstrate that BrickCraft acquires proficient assembly skills from a limited set of demonstrations and exhibits strong compositional generalization to unseen structures.

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2025

[C103] Eye-in-Finger: Smart Fingers for Delicate Assembly and Disassembly of LEGO
Zhenran Tang, Ruixuan Liu and Changliu Liu
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2025
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Abstract:

Manipulation and insertion of small and tight-toleranced objects in robotic assembly remain a critical challenge for vision-based robotics systems due to the required precision and cluttered environment. Conventional global or wrist-mounted cameras often suffer from occlusions when either assembling or disassembling from an existing structure. To address the challenge, this paper introduces "Eye-in-Finger", a novel tool design approach that enhances robotic manipulation by embedding low-cost, high-resolution perception directly at the tool tip. We validate our approach using LEGO assembly and disassembly tasks, which require the robot to manipulate in a cluttered environment and achieve sub-millimeter accuracy and robust error correction due to the tight tolerances. Experimental results demonstrate that our proposed system enables real-time, fine corrections to alignment error, increasing the tolerance of calibration error from 0.4mm to up to 2.0mm for the LEGO manipulation robot.

[W] NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing
Bowei Li, Peiqi Yu, Zhenran Tang, Han Zhou, Yifan Sun, Ruixuan Liu and Changliu Liu
RSS 2025 Workshop on Benchmarking Robot Manipulation: Improving Interoperability and Modularity, 2025
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Abstract:

This paper presents NeSyPack, a neuro-symbolic framework for bimanual logistics packing. Our NeSyPack combines data-driven models and symbolic reasoning to build an explainable hierarchical framework that is generalizable, data-efficient, and reliable. It decomposes a task into subtasks via hierarchical reasoning, and further into atomic skills managed by a symbolic skill graph. The graph selects skill parameters, robot configurations, and task-specific control strategies for execution. This modular design enables robustness, adaptability, and efficient reuse—outperforming end-to-end models that require large-scale retraining. Using NeSyPack, our team won the First Prize in the What Bimanuals Can Do (WBCD) competition at the 2025 IEEE International Conference on Robotics & Automation (ICRA).