RynnBrain (github.com)

RynnBrain has been unveiled as a cutting-edge embodied foundation model that integrates physical reality into its processing capabilities. It comes in two dense variants (2B and 8B) and a mixture-of-experts (MoE) model (30B-A3B). The release includes specialized models for robot task planning, vision-language navigation, and chain-of-point reasoning. RynnBrain excels at fine-grained video understanding and egocentric cognition, offering advanced capabilities in object localization, reasoning, and physical-space planning. Importantly, it employs an innovative interleaved reasoning strategy that combines textual and spatial inputs, ensuring robust interactions with the physical environment. This announcement is significant for the AI/ML community as it marks a substantial advancement in embodied AI, where models can comprehend and operate within dynamic real-world contexts. RynnBrain's architecture—a unified encoder-decoder framework for processing multi-modal inputs—addresses complex tasks that require intricate planning and execution based on environmental cues. Additionally, RynnBrain-Bench introduces a new high-dimensional benchmark to evaluate embodied understanding across critical dimensions, further establishing standards for future models in this rapidly progressing field.