Mbodi Diffusion Data Augmentation Beta Access

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Mbodi is a passionate supporter of open source and hopes to aid all roboticists in building the next generation of embodied agents. We believe that rivaling the performance of pure LLMs requires scalable data-acquisition techniques such as diffusion-based data augmentation and end-to-end data pipelines.

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Which of the following describes your views on Generative AI and Robotics? *

Excited and enthused to try!

Skeptical about positive transfer from different observation and action spaces

Skeptical about latency

Skeptical about reliability and accuracy

Concerned about data privacy

Prefer to build this in house

Other:

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What type of data augmentation are you most interested in? *

In-painting and Environment Change

Camera Angle Change: Replacing the video with the same video from a different camera angle

Embodiment Change: Replacing the robot and actions in a video to match your current robot.

Task Change: Replacing your robots actions and observations to match a different task in the same environment

Whatever gets me the best performance and reliability of my model

Other:

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What other features might you be interested in? *

End-to-End Learning: Create datasets in real time and train a custom or Mbodi's open source multi-modal model with just 10 lines of code.

Continual Learning: Train a model in the cloud by calling `mbodi.learn(observation, action)` anywhere in your stack

Curriculum Learning: Continually test different datasets and tasks to learn best curriculum

ChatGPT-like endpoint to return json objects representing actions for your robot to take.

Latency: Model distillation, speculative decoding, and semantic caching

Observability: Let me see how my model performs across a wide array of tasks and datasets as it updates

Jax Support

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