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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, Ziwei Wang

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. Our dataset, CAD files, code, and evaluation results can be found at: https://rocochallenge.github.io/RoCo2026/.

RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation

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. Our dataset, CAD files, code, and evaluation results can be found at: https://rocochallenge.github.io/RoCo2026/.
Paper Structure (43 sections, 4 equations, 8 figures, 1 table)

This paper contains 43 sections, 4 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Competition Structure, Rules and Results
  3. Simulation Track: Human-in-the-Loop by State Initialization
  4. Task Definitions and Data Collection
  5. Evaluation and Scoring
  6. Baseline and Data Provision
  7. Onsite Track: Physical Collaborative Assistance
  8. Hardware and Data Collection
  9. Real-World Constraints
  10. Baselines and Data Provision
  11. Results Overview
  12. Excellent VLA Solutions
  13. ARC-VLA and Failure-Aware Data Programming
  14. Architecture and "Risk-First" Perception
  15. Overcoming the "Lazy Robot" with L1 Loss
  16. ...and 28 more sections

Figures (8)

  • Figure 1: The simplified planetary gear reducer used in the RoCo Challenge, consisting of a sun gear, three planet gears, one finned key shaft, a ring gear component, and a planet carrier with three pins. The assembly task requires precise placement of these components within a planet carrier.
  • Figure 2: Simulation environment demonstrations for the three RoCo Challenge tasks. The stages mirror the real-world setup: (a) initial assembly, (b) completion from partial states, and (c) dynamic error recovery.
  • Figure 3: Visualization of the data frame for the simulation track. The dataset includes synchronized multi-modal observations (RGB, Depth, and proprioception) and action streams, enabling comprehensive training for both visuomotor imitation learning and VLA models.
  • Figure 4: Hardware settings for real-world data collection. The platform consists of a Galaxea A1X robot arm, multiple RealSense D405 cameras, a binocular camera, and an R1 Lite teleoperation device used for demonstration collection.
  • Figure 5: Real-world demonstration of the three gear assembly tasks in the RoCo Challenge. (a) Assembly from scratch; (b) Completion from a partial state; (c) Error recovery by replacing the incorrect gear.
  • ...and 3 more figures