Projects

Publications

2026

[U] RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation
Haichao Liu, Yuheng Zhou, Zhenyu Wu, Ziheng Ji, Ziyu Shan, Qianzhun Wang, Ruixuan Liu, Zhiyuan Yang, Yejun Gu, Shalman Khan, Shijun Yan, Jun Liu, Haiyue Zhu, Changliu Liu, Jianfei Yang, Jingbing Zhang and Ziwei Wang
arXiv:2603.15469, 2026
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Abstract:

Embodied Artificial Intelligence (EAI) is rapidly developing, gradually subverting previous autonomous systems’ paradigms from isolated perception to integrated, continuous action. This transition is highly significant for industrial robotic manipulation, promising to free human workers from repetitive, dangerous daily labor. To benchmark and advance this capability, we introduce the Robotic Collaborative Assembly Assistance (RoCo) Challenge with a dataset towards simulation and real-world assembly manipulation. Set against the backdrop of human-centered manufacturing, this challenge focuses on a high-precision planetary gearbox assembly task, a demanding yet highly representative operation in modern industry. Built upon a self-developed data collection, training, and evaluation system in Isaac Sim, and utilizing a dual-arm robot for real-world deployment, the challenge operates in two phases. The Simulation Round defines fine-grained task phases for step-wise scoring to handle the long-horizon nature of the assembly. The Real-World Round mirrors this evaluation with physical gearbox components and high-quality teleoperated datasets. The core tasks require assembling an epicyclic gearbox from scratch, including mounting three planet gears, a sun gear, and a ring gear. Attracting over 60 teams and 170+ participants from more than 10 countries, the challenge yielded highly effective solutions, most notably ARC-VLA and RoboCola. Results demonstrate that a dual-model framework for long-horizon multi-task learning is highly effective, and the strategic utilization of recovery-from-failure curriculum data is a critical insight for successful deployment. This report outlines the competition setup, evaluation approach, key findings, and future directions for industrial EAI.

2025

[J30] Certifying Robustness of Learning-Based Keypoint Detection and Pose Estimation Methods
Xusheng Luo, Tianhao Wei, Simin Liu, Ziwei Wang, Luis Mattei-Mendez, Taylor Loper, Joshua Neighbor, Casidhe Hutchison and Changliu Liu
ACM Transactions on Cyber-Physical Systems, 2025
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Abstract:

This work addresses the certification of the local robustness of vision-based two-stage 6D object pose estimation. The two-stage method for object pose estimation achieves superior accuracy by first employing deep neural network-driven keypoint regression and then applying a Perspective-n-Point (PnP) technique. Despite advancements, the certification of these methods’ robustness remains scarce. This research aims to fill this gap with a focus on their local robustness on the system level–the capacity to maintain robust estimations amidst semantic input perturbations. The core idea is to transform the certification of local robustness into neural network verification for classification tasks. The challenge is to develop model, input, and output specifications that align with off-the-shelf verification tools. To facilitate verification, we modify the keypoint detection model by substituting nonlinear operations with those more amenable to the verification processes. Instead of injecting random noise into images, as is common, we employ a convex hull representation of images as input specifications to more accurately depict semantic perturbations. Furthermore, by conducting a sensitivity analysis, we propagate the robustness criteria from pose to keypoint accuracy, and then formulating an optimal error threshold allocation problem that allows for the setting of a maximally permissible keypoint deviation thresholds. Viewing each pixel as an individual class, these thresholds result in linear, classification-akin output specifications. Under certain conditions, we demonstrate that the main components of our certification framework are both sound and complete, and validate its effects through extensive evaluations on realistic perturbations. To our knowledge, this is the first study to certify the robustness of large-scale, keypoint-based pose estimation given images in real-world scenarios.

[C86] ThinkBot: Embodied Instruction Following with Thought Chain Reasoning
Guanxing Lu, Ziwei Wang, Changliu Liu, Jiwen Lu and Yansong Tang
International Conference on Learning Representations, 2025
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2024

[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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[J26] Improve Certified Training with Signal-to-Noise Ratio Loss to Decrease Neuron Variance and Increase Neuron Stability
Tianhao Wei, Ziwei Wang, Peizhi Niu, Abulikemu Abuduweili, Weiye Zhao, Casidhe Hutchison, Eric Sample and Changliu Liu
Transactions on Machine Learning Research, 2024
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Abstract:

Neural network robustness is a major concern in safety-critical applications. Certified robustness provides a reliable lower bound on worst-case robustness, and certified training methods have been developed to enhance it. However, certified training methods often suffer from over-regularization, leading to lower certified robustness. This work addresses this issue by introducing the concepts of neuron variance and neuron stability, examining their impact on over-regularization and model robustness. To tackle the problem, we extend the Signal-to-Noise Ratio (SNR) into the realm of model robustness, offering a novel perspective and developing SNR-inspired losses aimed at optimizing neuron variance and stability to mitigate over-regularization. Through both empirical and theoretical analysis, our SNR-based approach demonstrates superior performance over existing methods on the MNIST and CIFAR-10 datasets. In addition, our exploration of adversarial training uncovers a beneficial correlation between neuron variance and adversarial robustness, leading to an optimized balance between standard and robust accuracy that outperforms baseline methods.

[C80] ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation
Guanxing Lu, Shiyi Zhang, Ziwei Wang, Changliu Liu, Jiwen Lu and Yansong Tang
European Conference on Computer Vision, 2024
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