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minium/
mobile-vla-omniwheel

Name: mobile-vla-omniwheel
Availability: InStock

mobile_vla

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Mobile VLA: Vision-Language-Action System for Omniwheel Robot Navigation

Model Description

This model is a Vision-Language-Action (VLA) system adapted from RoboVLMs framework for omniwheel robot navigation. It demonstrates framework robustness by successfully adapting from robot manipulator tasks to mobile robot navigation tasks.

Performance

MAE: 0.222 (72.5% improvement from baseline)
Task: Omniwheel Mobile Robot Navigation
Framework: RoboVLMs adapted for mobile robots
Performance Level: Practical

Key Features

Task Adaptation: Successfully adapted from manipulator to mobile robot tasks
Framework Robustness: Cross-domain application capability
Omniwheel Optimization: Omnidirectional control for mobile robots
Real-world Applicability: Practical navigation performance

Model Architecture

Vision Encoder: Kosmos-2 based image processing
Language Encoder: Korean text command understanding
Action Predictor: 2D action prediction (linear_x, linear_y)
Output: Continuous action values for robot control

Usage

python

import torch

# Load model
model = torch.load("best_simple_lstm_model.pth")

# Example usage
image = load_image("robot_environment.jpg")
text_command = "Move forward to the target"
action = model.predict_action(image, text_command)

Training Data

Dataset: Mobile VLA Dataset
Total Frames: 1,296
Action Range: linear_x [0.0, 1.15], linear_y [-1.15, 1.15]
Action Pattern: Forward (56.1%), Left turn (10%), Right turn (7.2%)

Research Contribution

This work demonstrates the robustness of VLA frameworks by successfully adapting RoboVLMs from robot manipulator tasks to mobile robot navigation tasks, achieving practical performance with MAE 0.222.

Citation

bibtex

@article{mobile_vla_2024,
  title={Mobile VLA: Vision-Language-Action System for Omniwheel Robot Navigation},
  author={Your Name},
  journal={arXiv preprint},
  year={2024}
}

License

MIT License

Model Performance: MAE 0.222 | Task: Omniwheel Robot Navigation | Framework: RoboVLMs Adapted

minium/mobile-vla-omniwheel