Unleashing Creativity with AI and Robots

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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StableLego provides data and analysis tools that predict whether complex brick structures will stand or collapse.

1. [U] BrickSim: A Physics-Based Simulator for Manipulating Interlocking Brick Assemblies
   Haowei Wen, Ruixuan Liu, Weiyi Piao, Siyu Li and Changliu Liu
   arXiv:2603.16853, 2026
   

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   Interlocking brick assemblies provide a standardized yet challenging testbed for contact-rich and long-horizon robotic manipulation, but existing rigid-body simulators do not faithfully capture snap-fit mechanics. We present BrickSim, the first real-time physics-based simulator for interlocking brick assemblies. BrickSim introduces a compact force-based mechanics model for snap-fit connections and solves the resulting internal force distribution using a structured convex quadratic program. Combined with a hybrid architecture that delegates rigid-body dynamics to the underlying physics engine while handling snap-fit mechanics separately, BrickSim enables real-time, high-fidelity simulation of assembly, disassembly, and structural collapse. On 150 real-world assemblies, BrickSim achieves 100% accuracy in static stability prediction with an average solve time of 5 ms. In dynamic drop tests, it also faithfully reproduces real-world structural collapse, precisely mirroring both the occurrence of breakage and the specific breakage locations. Built on Isaac Sim, BrickSim further supports seamless integration with a wide variety of robots and existing pipelines. We demonstrate robotic construction of brick assemblies using BrickSim, highlighting its potential as a foundation for research in dexterous, long-horizon robotic manipulation. BrickSim is open-source, and the code is available at https://github.com/intelligent-control-lab/BrickSim.

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BrickSim models the physics of interlocking bricks in real time, enabling high-fidelity simulation of assembly, disassembly, and structural collapse.

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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BrickGPT turns natural-language prompts into diverse brick structures that are stable, buildable, and ready to assemble.

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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Simulation-aided learning converts human demonstrations into reusable construction policies for new designs.

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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This framework learns assembly and disassembly directly from human demonstrations, then transfers those skills to robots.

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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A lightweight, low-cost end effector delivers robust assembly and disassembly across robot platforms.

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.

Eye-in-Finger places close-range vision at the tool tip for reliable submillimeter manipulation.

1. [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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   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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BrickCraft grounds and composes visuomotor skills from situated manuals, enabling robots to generalize to unseen assemblies.

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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Physics-aware action masking steers learning toward safe, executable sequences for complex assemblies.

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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APEX-MR coordinates multiple robots asynchronously for safe, efficient, long-horizon 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.

Optimized visual and language prompts enable vision-language models to detect failures, diagnose causes, and propose recovery actions.