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making robots smarter, faster
Seamlessly integrate AI into existing robotics stacks and continually improve.
current approaches to generative AI
consist of amassing huge amounts of expertly-collected data to train static foundation models. This process can take up to a year for a new model to be ready for production and is still unproven in robotics.
This neural network continuously adapts its behavior to "learn" how to predict the boundary, thus immediately improving behavior just as a human would. Enabling such a system that can be modified and tested on the fly will transform robotic industries.
we're building end-to-end learning systems that work today
Our technology is designed to change as little as possible of existing robotics software stacks, instead, delegating large language models to the control layers where they can be useful. Today, that layer is operator-robot interaction.
Intuitive Operator-Robot Interaction
With intra-context learning and advanced LLMs, Mbodi AI makes it easy for non-ML experts to teach robots new skills, provide guidance, and even troubleshoot issues using just voice commands.
Streamlined Learning Process
Every action and observation is processed to continually train your robot. Accelerate iterations and focus on innovation, reducing the time spent on repetitive tasks.
Improved Reliability with Generative Data
Standard models can struggle in new environments, with failure rates reaching 50%. Generative data augmentation cuts that by up to 80%, enhancing your product’s accuracy and performance in unfamiliar scenarios.
Intuitive Operator-Robot Interaction
With intra-context learning and advanced LLMs, Mbodi AI makes it easy for non-ML experts to teach robots new skills, provide guidance, and even troubleshoot issues using just voice commands.
Streamlined Learning Process
Every action and observation is processed to continually train your robot. Accelerate iterations and focus on innovation, reducing the time spent on repetitive tasks.
Improved Reliability with Generative Data
Standard models can struggle in new environments, with failure rates reaching 50%. Generative data augmentation cuts that by up to 80%, enhancing your product’s accuracy and performance in unfamiliar scenarios.
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who we're working with
frequently asked questions
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.
continually reduce cost every week your using mbodi ai, increase automation and throughput
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