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Vqbet Pusht

Developed by lerobot
VQ-BeT is a behavior generation model trained for the PushT environment, designed based on latent action principles
Downloads 68
Release Time : 7/3/2024

Model Overview

This model is based on the VQ-BeT architecture, specifically trained for the robot pushing task (PushT) environment, capable of generating action sequences for robot control

Model Features

Latent Action Representation
Uses vector quantization techniques to learn discrete latent action spaces, improving the robustness of behavior generation
Efficient Training
Achieves good performance after only 250,000 training steps, demonstrating high training efficiency
Multimodal Input Processing
Capable of processing RGB image inputs, suitable for real-world robot applications

Model Capabilities

Robot Action Generation
Visual Input Processing
Pushing Task Execution

Use Cases

Robot Control
Object Pushing Task
Controls the robot to push an object to a target position
In 500 tests, achieved an average maximum overlap rate of 89.5% and a success rate of 63.8%
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