Skip to main content

AutoRT - Pytorch

Project description

Multi-Modality

AutoRT

AutoRTImage Implementation of AutoRT: "AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents". This repo will implement the multi agent system that transforms a scene into a list of ranked and priortized tasks for an robotic action model to execute. This is an very effective setup that I personally believe is the future for swarming robotic foundation models!

This project will be implemented using Swarms, for the various llms and use the official RT-1 as the robotic action model.

PAPER LINK

Install

$ pip3 install autort-swarms

Usage

AutoRTAgent

A single AutoRT agent that: analyzes a scene using visual COT -> generate tasks -> filter tasks -> execute it with a robotic transformer.

# Import necessary modules
import os
from autort import AutoRTSwarm, AutoRTAgent

# Set the OpenAI API key
openai_api_key = os.getenv("OPENAI_API_KEY")

# Define a list of AutoRTAgent instances
agents = [
    AutoRTAgent(openai_api_key, max_tokens=1000),
    AutoRTAgent(openai_api_key, max_tokens=1000),
]

# Create an instance of AutoRTSwarm with the agents and datastore
autort_swarm = AutoRTSwarm(agents)

# Run the AutoRTSwarm with the given inputs
autort_swarm.run(
    "There is a bottle on the table.",
    "https://i.imgur.com/2qY9f8U.png",
)

AutoRTSwarm

A team of AutoRT agents where you can plug in and play any number of AutoRTAgents with customization. First, the task will be routed to each agent and then all of their outputs will be saved.

# Import necessary modules
import os
from autort import AutoRTSwarm, AutoRTAgent

# Set the OpenAI API key
openai_api_key = os.getenv("OPENAI_API_KEY")

# Define a list of AutoRTAgent instances
agents = [
    AutoRTAgent(openai_api_key, max_tokens=1000),
    AutoRTAgent(openai_api_key, max_tokens=1000),
]

# Create an instance of AutoRTSwarm with the agents and datastore
autort_swarm = AutoRTSwarm(agents)

# Run the AutoRTSwarm with the given inputs
autort_swarm.run(
    "There is a bottle on the table.",
    "https://i.imgur.com/2qY9f8U.png",
)

Citation

@inproceedings{
    anonymous2023autort,
    title={Auto{RT}: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents},
    author={Anonymous},
    booktitle={Submitted to The Twelfth International Conference on Learning Representations},
    year={2023},
    url={https://openreview.net/forum?id=xVlcbh0poD},
    note={under review}
}

License

MIT

Todo

  • Implement a run method into AutoRTSwarm that runs all the agents with APIs.
  • Make it able to send commands to a certain agent using the swarm network.
  • Send a task to all agents in the swarm network

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autort_swarms-0.0.4.tar.gz (9.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

autort_swarms-0.0.4-py3-none-any.whl (9.1 kB view details)

Uploaded Python 3

File details

Details for the file autort_swarms-0.0.4.tar.gz.

File metadata

  • Download URL: autort_swarms-0.0.4.tar.gz
  • Upload date:
  • Size: 9.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.11.0 Darwin/23.3.0

File hashes

Hashes for autort_swarms-0.0.4.tar.gz
Algorithm Hash digest
SHA256 260070cad9195bd2a01155046200902d2b15d2e7a7146ba98072b457422a419a
MD5 341f95ae27bc028ae389aae9cd75292b
BLAKE2b-256 315961221b9961cf89235b4f89e23497341e0a2b025e0dc63e77c364b1a432e1

See more details on using hashes here.

File details

Details for the file autort_swarms-0.0.4-py3-none-any.whl.

File metadata

  • Download URL: autort_swarms-0.0.4-py3-none-any.whl
  • Upload date:
  • Size: 9.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.11.0 Darwin/23.3.0

File hashes

Hashes for autort_swarms-0.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 0279ead922b9df3f716741bbf0b84b8c4b3b601b9f02b33b12b1dded402ed99b
MD5 bb7e42a8e2c648bb850e998599af9df6
BLAKE2b-256 24f7ef04068e144287eb9fe5215eb702713ed0dbee816376d77c81a5168a46d7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page