Merge pull request #2 from MbodiAI/readme

readme update

README.md

@@ -7,24 +7,55 @@
 [![Ubuntu](https://github.com/MbodiAI/opensource/actions/workflows/ubuntu.yml/badge.svg)](https://github.com/MbodiAI/opensource/actions/workflows/ubuntu.yml)
 [![Example Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DAQkuuEYj8demiuJS1_10FIyTI78Yzh4?usp=sharing)
 
-Welcome to **Mbodied Agents**! This repository is your gateway to integrating generative AI and various transformers with robotics. By leveraging vision-language models, transformers, and a robust data handling infrastructure, Mbodied Agents provides a comprehensive, versatile, and easy-to-use platform for diverse environments and embodiments.
+# Mbodied Agents </br> Bringing the Power of Generative AI to Robotics
+
+<img src="assets/logo.jpeg" alt="Mbodied Agents Logo" style="width: 200px;">
+
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![MacOS | Python 3.12|3.11|3.10](https://github.com/MbodiAI/opensource/actions/workflows/macos.yml/badge.svg?branch=main)](https://github.com/MbodiAI/opensource/actions/workflows/macos.yml)
+[![Ubuntu](https://github.com/MbodiAI/opensource/actions/workflows/ubuntu.yml/badge.svg)](https://github.com/MbodiAI/opensource/actions/workflows/ubuntu.yml)
+[![Example Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DAQkuuEYj8demiuJS1_10FIyTI78Yzh4?usp=sharing)
+
+Welcome to **Mbodied Agents**, a toolkit for integrating various state-of-the-art transformers in robotics. We wanted to make a consistent interface for calling different AI models, handling multimodal data, and using/creating datasets trained on different robots. Be sure to checkout the [examples](examples/README.md) for how to automatically fine-tune a foundational model in as little as 10 lines of code.  With that in mind, Mbodied Agents offers the following features:
+
+- **Configurability** : Define your desired Observation and Action spaces and read data into the format that works best for your system.
+- **Natural Language Control** : Use verbal prompts to correct a [cognitive agent](link here)'s actions and calibrate its behavior to a new environment.
+- **Modularity** : Easily swap out different backends, transformers, and hardware interfaces. For even better results, run multiple agents in separate threads.
+- **Validation** : Ensure that your data is in the correct format and that your actions are within the correct bounds before sending them to the robot.
+
+The core idea idea behind Mbodied Agents is end-to-end continual learning. We believe that the best way to train a robot is to have it learn from its own experiences.
+This is why we are building out a feature called __Conductor__ to record your conversation and the robot's actions as you interact with/teach the robot. This data can then be used to train a foundational model, which can be fine-tuned on your own data. This is a powerful tool for robotics research and development, as it allows you to train a robot to perform a task in a new environment without having to retrain the model from scratch.
+
+## Support Matrix
+
+If you would like to integrate a new backend, sense, or motion control, it is very easy to do so. Please refer to the [contributing guide](CONTRIBUTING.md) for more information.
+
+- OpenAI
+- Anthropic
+- Mbodi (Coming Soon)
+- HuggingFace (Coming Soon)
+
+## In Beta
+
+For access (or just to say hey (: ), please fill out this [form](https://forms.gle/rv5rovK93dLucma37) or reach out us at [email protected].
+
+- **Conductor** : We are building out a service for automatically training your own models on your own data. We believe this will be a game-changer for robotics and AI research.
+- **Mbodied SVLM** : We are currently working on a new Spatial Vision Language Model trained specifically for spatial reasoning and robotics control.
+- **FAISS Indexing** : Use FAISS to index your robot's recent memory and perform RAG rather than pollute its context.
+
+## Roadmap
+
+- **Data Augmentation** : Build invariance to different environments by augmenting your dataset with Mbodi's diffusion-based data augmentation.
+- **Conductor Dashboard** : See how GPT4o, Claude Opus, or your custom models are performing on your datasets and open benchmarks.
+- **Few Shot Visual Prompting** : Use verbal or visual prompts to correct your robot's actions and calibrate its behavior to a new environment.
 
-You can simply command and teach any robot to do anything while collecting datasets!
 
 <img src="assets/architecture.jpg" alt="Architecture Diagram" style="width: 650px;">
 
 Each time you interact with a robot, the data is automatically recorded into a dataset, which can be augmented and used for model training, without wasting any conversation or action. To learn more about how to use the dataset, augment the data, or train/finetune a foundational model, please fill out this [form](https://forms.gle/rv5rovK93dLucma37) or reach out to us at [email protected].
 
 <img src="assets/demo_gif.gif" alt="Demo GIF" style="width: 625px;">
 
-Upcoming Features:
-
-- Mbodi's backend
-- HuggingFace backend
-- Mbodi diffusion-based data augmentation backend
-- Mbodi image 3D segmentation backend
-- Dataset replayer
-- And much more! Stay tuned.
 
 We welcome any questions, issues, or PRs! Refer to the Contributing section below for more details.
 
@@ -34,6 +65,10 @@ Please join our [Discord](https://discord.gg/RNzf3RCxRJ) for interesting discuss
 
 ## Installation
 
+```pip install mbodied-agents```
+
+## Dev Environment Setup
+
 1. Clone this repo:
 
    ```console
@@ -52,7 +87,7 @@ Please join our [Discord](https://discord.gg/RNzf3RCxRJ) for interesting discuss
    hatch shell
    ```
 
-## Get Started
+## Getting Started
 
 Please refer to [examples/simple_robot_agent.py](examples/simple_robot_agent.py) or use the Colab below to get started.