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embody

v0.0.5

Published

A digital consciousness training environment

Downloads

5

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Links

Maintainers

piusnyakoojopiusnyakoojo

Keywords

embodydigitalconsciousnessartificialintelligenceembodimentsensoractuatorlearningenvironment

Readme

🌎 embody

npm version downloads coverage pipeline

Demo | Video Tutorial | Paper

embody is a sensor and actuator environment for training digital consciousness (also known as artificial intelligence) algorithms.

This project is a work-in-progress and does not completely work as suggested by this document. Parts of the system work as suggested, other parts are still being worked on.

About

Embody is a set of interfaces to sensors and actuators on a computer. The interfaces provide a way to receive or update data about the state of a computer.

Sensor interfaces retrieve data about the state of a computer. For example: a screen capture video stream, mouse movement events and keyboard press events. Actuator interfaces update the state of a computer. For example: moving the mouse and pressing a keyboard button.

Benefits

  • 💪 Flexible: Learn from across any number of sense, action and interface configurations

Sensor Features

  • 👁️ Sight: See the world through a screen capture video stream, webcam or camera
  • 👂 Hearing: Use a microphone to listen to the world (WIP)
  • 🖐️ Touch: Feel through heat, texture and force feedback sensors (WIP)
  • 👃 Smell: Respond to air quality sensors to model scent (WIP)
  • 👅 Taste: Respond to water quality sensors to model taste (WIP)

Actuator Features

  • 🖼️ Imagination: Create images on a 2D canvas by painting with pixels (WIP)
  • 🙌 Movement: Move in the world using a mouse, keyboard or motor
  • 👄 Speech: Create voice patterns to be played by speakers (WIP)

Limitations

  • 🧪 Experimental: This project is a work-in-progress

Screenshots

Installation

Install the library

npm install embody -g

Example Usecases

API Reference

Documentation

To check out project-related documentation, please visit docs

Contributing

Feel free to join in. All are welcome. Please see contributing guide.

Acknowledgements

Learn More

Related Work

Some well-known artificial intelligence training environments include: Conceptual Learning (VicariousAI PixelWorld), Reinforcement Learning (OpenAI Gym, DeepMind Lab, garage), Algorithm-Agnostic (ROS, Embody)

License

MIT