Summary

RynnBrain introduces open embodied foundation models grounded in physical reality, designed for researchers and engineers in robotics and AI. It offers comprehensive egocentric understanding, spatio-temporal localization, and physics-aware planning, enabling more robust and nuanced interaction with the physical world.

How It Works

The project utilizes a unified encoder-decoder architecture, available in Dense (2B, 8B) and Mixture-of-Experts (30B-A3B) variants. It processes omni-vision and textual inputs to generate multi-modal outputs, including spatial trajectories and action plans. Training on extensive spatio-temporal, physical-space, and general knowledge data underpins its capabilities. Key innovations include an interleaved reasoning strategy that grounds textual understanding in physical space and physics-aware planning that integrates object affordances for complex task execution.

Quick Start & Requirements

Installation is straightforward via pip install transformers==4.57.1. Minimal shell dependencies are required. Comprehensive examples and use-case demonstrations are available in the provided cookbooks.

Highlighted Details

• Comprehensive egocentric understanding: Excels in fine-grained video understanding and egocentric cognition, covering embodied QA, counting, and OCR.
• Diverse spatio-temporal localization: Possesses powerful localization capabilities across episodic memory, enabling precise identification of objects, target areas, and motion trajectories.
• Physical-space reasoning: Employs an interleaved reasoning strategy that alternates between textual and spatial grounding, ensuring reasoning is rooted in the physical environment.
• Physics-aware precise planning: Integrates located affordances and object information into planning, enabling downstream VLA models to execute intricate tasks with fine-grained instructions.
• RynnBrain-Bench: A high-dimensional benchmark for embodied understanding evaluating object cognition, spatial cognition, grounding, and pointing.

Maintenance & Community

The project is part of an active ecosystem with several related GitHub repositories and arXiv papers, including RynnEC, RynnScale, RynnVLA-001, RynnVLA-002, RynnRCP, and RynnMotion. This indicates ongoing development and research by the Alibaba Damo Academy team. No direct community channels (e.g., Discord, Slack) or roadmap links are provided in the README.

Licensing & Compatibility

The project is licensed under the Apache License 2.0, which is permissive for commercial use and integration into closed-source projects.

Limitations & Caveats

The provided README does not explicitly detail any limitations, known bugs, or alpha/beta status of the RynnBrain models.