Industrial assembly represents a core of manufacturing, poses significant challenges to the reliability and adaptability of robot systems. As manufacturing shifts toward intelligent production, there is an urgent need for efficient human-to-robot skill transfer methods for high-precision assembly tasks. However, current embodied intelligence research has primarily focused on household tasks, industrial applications involving dynamic uncertainties and strict control demands remain largely unexplored. To bridge this gap, we propose a real-world skill transfer framework and establish a contact-rich assembly benchmark. It integrates an MR-assisted demonstration system for low-cost and diverse data collection, an end-to-end visuomotor imitation learning algorithm for continuous action prediction, and a primitive skill library covering industrially-inspired tasks such as peg insertion, gear meshing, and disassembly. Experiments on six tasks demonstrate high success rates and robust positional generalization. This study explores a novel pathway, it is hoped that this work will provide valuable insights for future human-robot collaboration, and serve as a critical precursor for the integration of Industry 5.0 with embodied intelligence.
Robot programming in assembly tasks often requires domain expertise and extensive parameter tuning, posing barriers for non-expert users. To improve accessibility and interactivity, we developed an MR-based human-robot interaction interface leveraging head-mounted displays, MR technology, and contact force simulation for direct and safe robot teleoperation and demonstration, facilitating state-action datasets for end-to-end imitation learning.
This work implements two complementary teleoperation modes for demonstration collection: 1) MR-based position control and 2) gamepad-based velocity control. The MR control enables precise 6-DoF trajectory demonstrations via spatial mapping, while gamepad-based velocity control ensures stable, millimeter-scale screw-fastening through fine-grained force adjustment.
The assembly tasks include peg insertion, gear meshing, and nut screwing, designed with reference to the Factory simulated benchmark. In each task, the robot must grasp parts from the task board and insert them precisely into predefined positions. To evaluate robustness, perceptual variations were introduced during data collection, changing visual inputs while preserving core task objectives.
Unlike conventional pick-and-place operations, this task requires a downward motion with insertion force. The peg features chamfers on both ends to facilitate guidance and alignment. Components are randomly positioned within four regions p1~ p4 of the task board.
The robot assembles a small gear onto a shaft and aligns it with a neighboring gear, requiring high accuracy in both orientation and position.
Due to the tight clearance between the nut and bolt, the task involves multi-surface frictional contact with 6-DoF motion. The initial height of the nut relative to the bolt varies in 5 mm increments.
The disassembly tasks, including gear-peg removal, snap-fit disassembly, and U-peg reassembly, are inspired by the part configurations in the AutoMate dataset. The robot must remove each component from the pre-assembled task board and place it into designated containers. In task variants, the target base alternates among three predefined positions p1~ p3 to test positional robustness.
The robot extracts a peg from the gear assembly and places it into a storage box.
The robot extracts the snap-fit component to release the locking mechanism and places it into the transparent storage box.
Involving two identical bases, the robot first removes a U-peg from one base and inserts it into the other, requiring high-precision alignment.
Fine-grained manipulation tasks such as assembly pose substantial challenges for robots due to their stringent requirements on precision and coordination. Traditional solutions often depend on expensive sensors and complex calibration procedures. Imitation learning offers a more flexible alternative but suffers from error accumulation and distributional shift caused by the non-stationarity of human demonstrations. To address these challenges, this work introduces an efficient imitation learning framework that enables robust action sequence prediction from multimodal observations.
The following videos demonstrate real-world execution of assembly and disassembly tasks from both wrist camera and main camera perspectives, showcasing the precision and reliability of the learned policies across different manipulation primitives.
Peg Insertion
Gear Meshing
Screw Fastening
Wrist Camera View
Main Camera View
Gear-peg Removal
Snap-fit Disassembly
U-peg Reassembly
Wrist Camera View
Main Camera View
This study presents a systematic ablation analysis on peg insertion to assess the effects of data scale, input preprocessing, and network architecture, using the original ACT model as baseline. Experiments follow a unified protocol with 24 trials per setting across four workspace regions to evaluate spatial generalization. Aggregated results are shown in the table below.
| Configuration | Training Data | Input | Epochs | Backbone | Left Success | Right Success | Overall Success |
|---|---|---|---|---|---|---|---|
| ACT (Baseline) | 50 demos | 480×640 | 50K | ResNet | 23.5% | 20.0% | 21.9% |
| + Augmented Data | 100 demos | 480×640 | 100K | ResNet | 41.7% | 41.7% | 41.7% |
| + Cropped Input | 50 demos | 224×224 | 100K | ResNet | 58.3% | 83.3% | 70.8% |
| + Vision Transformer | 50 demos | 224×224 | 100K | ViT | 75.0% | 83.3% | 79.2% |
To evaluate spatial generalization, we assess policy performance across different workspace regions and task variants. The following results demonstrate success rates and failure modes for ACT-based policies on assembly tasks.
Failure Modes: GG-BF = Good Grasp & Bad Fit, SL = Slipped, PM = Partial Meshing/Misalignment, MT = Missed Target, PS = Position Shift
Note: For Peg Insertion and Gear Mesh, positions refer to workspace corners (P1: Top-Left, P2: Bottom-Left, P3: Top-Right, P4: Bottom-Right). For Screw Fastening, positions refer to initial height variations (Default, +5mm, +10mm, +15mm, +20mm).
Attention Visualization: To address the interpretability challenge in end-to-end approaches, we employ Grad-CAM to analyze the model's learned representations. As shown in below, our model accurately focuses on task-critical elements such as pins, holes, and small gears, effectively capturing manipulation-relevant regions. This demonstrates that using first-person data effectively narrows the domain gap between training and deployment, enabling more efficient human-to-robot skill transfer.
Trajectory Visualization: The figure below presents the trajectory visualization results of the proposed imitation learning framework across three representative assembly and disassembly tasks. Each subfigure illustrates the end-effector's complete 3D motion trajectory, with color gradients representing temporal evolution and clearly annotated critical points (initial, intermediate, and target positions).
If you find it helpful, please consider citing our work:
@article{wu2025H2R-MRSTA,
title={H2R-MRSTA: A Mixed Reality-Assisted Human-to-Robot Skill Transfer Approach for Contact-rich Assembly},
author={Wu,Duidi and Zhao,Qianyou and Shen,Yuliang and Li,Junlai and Zheng,Pai and Qi,Jin and Hu,Jie},
journal={preprint},
year={2024}
}