Large Behaviour Models (LBM)

AI systems designed to understand, simulate, and generate complex behavioural patterns and sequential actions by learning from multimodal observational data including video, sensor inputs, and empirical experience. Unlike Large Language Models that process text to generate language, LBMs analyse real-world behaviours to predict and replicate decision-making processes, contextual actions, and physical interactions. **Key characteristics:** - Learn from multimodal data sources (video, sensors, text) rather than text alone - Focus on sequential actions and decision-making processes rather than language generation - Trained through observational learning and empirical experience, often using diffusion policy methods - Enable embodied AI applications requiring physical interaction and spatial awareness - Process behavioural patterns including preferences, contextual responses, and action sequences **Differs from LLM:** - **Training data:** LBMs learn from visual, sensor, and experiential data whilst LLMs train exclusively on text corpora - **Output focus:** LBMs generate actions and behavioural predictions whilst LLMs produce language-based responses - **Application domain:** LBMs excel in physical interaction contexts (robotics, healthcare engagement) whilst LLMs specialise in conversational and text-based tasks **Applications:** - Robotics and autonomous systems requiring complex manipulation and navigation - Healthcare consumer engagement through personalised behavioural predictions - Customer journey optimisation by predicting user actions and preferences - Dexterous manipulation tasks in manufacturing and service environments - Sequential decision-making in dynamic, unpredictable environments **Business value:** LBMs enable organisations to move beyond conversational AI into applications requiring physical interaction, behavioural prediction, and real-world action execution. By understanding and replicating complex human behaviours, businesses can deploy AI systems for robotics, personalised healthcare interventions, and customer engagement scenarios where sequential actions and contextual decision-making create measurable operational value.