Enterprise AI Analysis
Data-Driven Dynamic Parameter Learning of Manipulator Robots
This paper presents a Transformer-based approach for accurate and generalizable dynamic parameter estimation in manipulator robots. It leverages an automated pipeline for diverse robot model generation and enriched trajectory data, and demonstrates strong performance in estimating inertial and friction parameters, crucial for bridging the sim-to-real gap in robotics.
Key Executive Impact
Quantifiable results demonstrating the power of data-driven AI in advanced robotics.
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Validation R² (Optimal Model)
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Robots Generated
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Effective Time Window (s)
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Methodology Overview
The core methodology involves an automated pipeline for generating varied robot models (URDFs), simulating diverse trajectories, enriching data with kinematic features, and finally training a Transformer-based model.
Experimental Results
Experimental results show the influence of sequence length, sampling rate, and architectural choices on model performance. The optimal configuration achieved a validation R² of 0.8633.
Enterprise Impact
Accurate dynamic parameter estimation is crucial for reliable model-based control, realistic simulation, and safe deployment of manipulators, effectively bridging the sim-to-real gap.
Automated Data Generation Pipeline
Robot Generator (URDFs)
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Trajectory Generator (Waypoints)
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ROS-Gazebo Simulation
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Dataset Preprocessing
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Deep Learning (Transformer Model)
Optimal Model Performance
0.8633Validation R² (Optimal Configuration)
The best model achieved a validation R² of 0.8633 with a sequence length of 64, sampling rate of 64 Hz, 4 layers, 32 heads, and an embedding size of 128.
| Parameter Type | Key Findings |
|---|---|
| Inertia Parameters & Mass |
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| Coulomb Friction |
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| Viscous Friction & COM |
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Impact on Sim-to-Real Transfer
Bridging the Reality Gap
Accurate dynamic parameter estimation is fundamental for bridging the sim-to-real gap. By creating simulations that closely match real-world physics, this research enables more reliable transfer of control policies and reinforcement learning agents from simulation to physical robots, enhancing safety and efficiency. This also supports the development of high-quality digital twins for predictive maintenance and optimization.
The proposed method significantly contributes to improved sim-to-real transfer and adaptive control in robotic systems.
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