Visualnav-Transformer

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visualnav-transformer is an open-source AI Agents & Automation tool that provides code and checkpoints for mobile robot foundation models. It enables the development of goal-conditioned visual navigation policies for various robots.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is visualnav-transformer?

visualnav-transformer offers official code and checkpoint releases for mobile robot foundation models, including GNM, ViNT, and NoMaD. This open-source repository facilitates the training of these models with custom data and their deployment on robots like TurtleBot2/LoCoBot. It supports diverse, cross-embodiment training data and allows for efficient fine-tuning or adaptation to new robots and tasks. The tool provides scripts for processing datasets, training models, and deploying them for navigation and exploration, making it a comprehensive solution for robotics researchers and AI developers in the field of visual navigation.

Best used for

Ideal for robotics researchers and AI developers who need to train visual navigation models, deploy them on mobile robots, and create topological maps for autonomous navigation. Especially valuable for those working with diverse datasets and requiring efficient model adaptation to various robot platforms.

Common actions

train navigation models

deploy robot policies

process robot data

create topological maps

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Capabilities

Key features

GNM, ViNT, NoMaD models
Custom data training
Pre-trained model checkpoints
Robot deployment scripts
Topological map creation
Cross-embodiment training

Target Audience

professordeveloper

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What mobile robot foundation models are included in visualnav-transformer?

visualnav-transformer includes official code and checkpoint releases for General Navigation Models (GNM), Visual Navigation Transformer (ViNT), and Goal Masking Diffusion Policies for Navigation and Exploration (NoMaD). These models are designed for general-purpose, goal-conditioned visual navigation.

Can I train these models with my own custom data?

Yes, the repository provides scripts and guidelines for processing your own datasets, including rosbags or custom HDF5 formats, to train or fine-tune the GNM, ViNT, and NoMaD models. This allows for adaptation to specific environments and robot types.

What are the hardware and software requirements for deploying the models?

Deployment is tested on a LoCoBot running Ubuntu 20.04 with ROS Noetic, Python 3.7+, and a GPU with CUDA 10+. It requires a wide-angle RGB camera and a joystick/keyboard for teleoperation. The codebase also assumes access to conda for environment management.

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Research & Education › Academic Research Coding & Development › Open Source & Models

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