enabling internet scale learning in robotics
whether you're a hobbyist, researcher, or robotics company
Unique human-robot interface
Through intra-context learning and powerful LLMs, mbodi ai enables non-ML experts to teach new skills, provide oversight, and even diagnose problems using only voice and demonstration.
End-to-end learning
Every visual observation and action taken is pipelined to continually train your robot, Agent, or World Model. Iterate through experiments faster, so you can dedicate your time to what matters.
Generative Data Augmentation
Even SOTA models can face up to a 50% failure rate in unfamiliar settings. Early research indicates that generative data augmentation can decrease this failure rate by 80%, significantly improving both reliability and accuracy in unfamiliar settings.
diffusion based generative data augmentation
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FAQs
How does Mbodi AI lead to cost savings?
Mbodi AI streamlines the data collection process, enabling both lower ML expertise and volume of task-specific data requirements to match in-distribution performance. This efficiency allows teams to devote more resources to iterating on experiments and refining outputs, ultimately saving costs and accelerating the development cycle.
Are large language models inherently too slow for robotics?
While Large Language Models (LLMs) are traditionally associated with slower processing times, Mbodi AI's compositional semantic caching, quantization, and model distillation techniques ensure your robot remains rapid and responsive, even in complex analytical tasks.
Why Not Solely Rely on Simulations?
Real-world experience provides the richest data for refining AI models. Mbodi AI prioritizes actual operational data, utilizing data augmentation only to build upon this foundation. This process effectively filters out inconsequential variations between environments, focusing instead on meaningful data that drives precision and relevance in outcomes.
enabling internet scale learning in robotics
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