We integrate physical constraints into generative systems so that produced designs are stable and buildable. StableLego provides more than 50,000 assembly structures and stability inferences for studying this problem.

1. [J27] StableLego: Stability Analysis of Block Stacking Assembly
   Ruixuan Liu, Kangle Deng, Ziwei Wang and Changliu Liu
   IEEE Robotics and Automation Letters, 2024
   

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BrickGPT explores end-to-end design generation with foundation models, while our learning-from-demonstration work extracts new designs directly from human construction sequences.

1. [C106] Generating Physically Stable and Buildable Brick Structures from Text
   Ava Pun, Kangle Deng, Ruixuan Liu, Deva Ramanan, Changliu Liu and Jun-Yan Zhu
   International Conference on Computer Vision, 2025
   Best Paper Award (Marr Prize)
   

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   We introduce LegoGPT, the first approach for generating physically stable LEGO brick models from text prompts. To achieve this, we construct a large-scale, physically stable dataset of LEGO designs, along with their associated captions, and train an autoregressive large language model to predict the next brick to add via next-token prediction. To improve the stability of the resulting designs, we employ an efficient validity check and physics-aware rollback during autoregressive inference, which prunes infeasible token predictions using physics laws and assembly constraints. Our experiments show that LegoGPT produces stable, diverse, and aesthetically pleasing LEGO designs that align closely with the input text prompts. We also develop a text-based LEGO texturing method to generate colored and textured designs. We show that our designs can be assembled manually by humans and automatically by robotic arms. We also release our new dataset, StableText2Lego, containing over 47,000 LEGO structures of over 28,000 unique 3D objects accompanied by detailed captions, along with our code and models at the project website: this URL.

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1. [U] Simulation-aided Learning from Demonstration for Robotic LEGO Construction
   Ruixuan Liu, Alan Chen, Xusheng Luo and Changliu Liu
   arXiv:2309.11010, 2023
   

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1. [W] Robotic LEGO Assembly and Disassembly from Human Demonstration
   Ruixuan Liu, Yifan Sun and Changliu Liu
   ACC Workshop on Recent Advancement of Human Autonomy Interaction and Integration, 2023
   

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Bricks are small relative to conventional robot grippers. We design mechanical end-of-arm tools that allow general-purpose robots to assemble and disassemble them robustly.

1. [C76] A Lightweight and Transferable Design for Robust LEGO Manipulation
   Ruixuan Liu, Yifan Sun and Changliu Liu
   International Symposium of Flexible Automation, 2024
   

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1. [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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   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.

We develop physics-aware assembly sequencing and cooperative manipulation methods for complex structures that cannot be completed reliably by a single robot.

1. [J29] Physics-Aware Combinatorial Assembly Sequence Planning using Data-free Action Masking
   Ruixuan Liu, Alan Chen, Weiye Zhao and Changliu Liu
   IEEE Robotics and Automation Letters, 2025
   

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   Combinatorial assembly uses standardized unit primitives to build objects that satisfy user specifications. This paper studies assembly sequence planning (ASP) for physical combinatorial assembly. Given the shape of the desired object, the goal is to find a sequence of actions for placing unit primitives to build the target object. In particular, we aim to ensure the planned assembly sequence is physically executable. However, ASP for combinatorial assembly is particularly challenging due to its combinatorial nature. To address the challenge, we employ deep reinforcement learning to learn a construction policy for placing unit primitives sequentially to build the desired object. Specifically, we design an online physics-aware action mask that filters out invalid actions, which effectively guides policy learning and ensures violation-free deployment. In the end, we apply the proposed method to Lego assembly with more than 250 3D structures. The experiment results demonstrate that the proposed method plans physically valid assembly sequences to build all structures, achieving a 100% success rate, whereas the best comparable baseline fails more than 40 structures.

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1. [C96] APEX-MR: Multi-Robot Asynchronous Planning and Execution for Cooperative Assembly
   Philip Huang, Ruixuan Liu, Shobhit Aggarwal, Changliu Liu and Jiaoyang Li
   Robotics: Science and Systems, 2025
   

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   Compared to a single-robot workstation, a multi-robot system offers several advantages: 1) it expands the system’s workspace, 2) improves task efficiency, and more importantly, 3) enables robots to achieve significantly more complex and dexterous tasks, such as cooperative assembly. However, coordinating the tasks and motions of multiple robots is challenging due to issues, e.g., system uncertainty, task efficiency, algorithm scalability, and safety concerns. To address these challenges, this paper studies multi-robot coordination and proposes APEX-MR, an asynchronous planning and execution framework designed to safely and efficiently coordinate multiple robots to achieve cooperative assembly, e.g., LEGO assembly. In particular, APEX-MR provides a systematic approach to post-process multi-robot tasks and motion plans to enable robust asynchronous execution under uncertainty. Experimental results demonstrate that APEX-MR can significantly speed up the execution time of many long-horizon LEGO assembly tasks by 48% compared to sequential planning and 36% compared to synchronous planning on average. To further demonstrate the performance, we deploy APEX-MR to a dual-arm system to perform physical LEGO assembly. To our knowledge, this is the first robotic system capable of performing customized LEGO assembly using commercial LEGO bricks. The experiment results demonstrate that the dual-arm system, with APEX-MR, can safely coordinate robot motions, efficiently collaborate, and construct complex LEGO structures.

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Multimodal perception and foundation models monitor execution, identify potential failures, and select recovery strategies before errors cascade through the assembly.

1. [C87] Automating Robot Failure Recovery Using Vision-Language Models With Optimized Prompts
   Hongyi Chen, Yunchao Yao, Ruixuan Liu, Changliu Liu and Jeffrey Ichnowski
   American Control Conference, 2025
   

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   Current robot autonomy struggles to operate beyond the assumed Operational Design Domain (ODD), the specific set of conditions and environments in which the system is designed to function, while the real-world is rife with uncertainties that may lead to failures. Automating recovery remains a significant challenge. Traditional methods often rely on human intervention to manually address failures or require exhaustive enumeration of failure cases and the design of specific recovery policies for each scenario, both of which are labor-intensive. Foundational Vision-Language Models (VLMs), which demonstrate remarkable common-sense generalization and reasoning capabilities, have broader, potentially unbounded ODDs. However, limitations in spatial reasoning continue to be a common challenge for many VLMs when applied to robot control and motion-level error recovery. In this paper, we investigate how optimizing visual and text prompts can enhance the spatial reasoning of VLMs, enabling them to function effectively as black-box controllers for both motion-level position correction and task-level recovery from unknown failures. Specifically, the optimizations include identifying key visual elements in visual prompts, highlighting these elements in text prompts for querying, and decomposing the reasoning process for failure detection and control generation. In experiments, prompt optimizations significantly outperform pre-trained Vision-Language-Action Models in correcting motion-level position errors and improve accuracy by 65.78% compared to VLMs with unoptimized prompts. Additionally, for task-level failures, optimized prompts enhanced the success rate by 5.8%, 5.8%, and 7.5% in VLMs’ abilities to detect failures, analyze issues, and generate recovery plans, respectively, across a wide range of unknown errors in Lego assembly.