RynnEC: Bringing MLLMs into Embodied World
Authors: name: Ronghao Dang, name: Yuqian Yuan, name: Yunxuan Mao, name: Kehan Li, name: Jiangpin Liu, name: Zhikai Wang, name: Xin Li, name: Fan Wang, name: Deli Zhao

We introduce RynnEC, a video multimodal large language model designed for
embodied cognition. Built upon a general-purpose vision-language foundation
model, RynnEC incorporates a region encoder and a mask decoder, enabling
flexible region-level video interaction. Despite its compact architecture,
RynnEC achieves state-of-the-art performance in object property understanding,
object segmentation, and spatial reasoning. Conceptually, it offers a
region-centric video paradigm for the brain of embodied agents, providing
fine-grained perception of the physical world and enabling more precise
interactions. To mitigate the scarcity of annotated 3D datasets, we propose an
egocentric video based pipeline for generating embodied cognition data.
Furthermore, we introduce RynnEC-Bench, a region-centered benchmark for
evaluating embodied cognitive capabilities. We anticipate that RynnEC will
advance the development of general-purpose cognitive cores for embodied agents
and facilitate generalization across diverse embodied tasks. The code, model
checkpoints, and benchmark are available at:
https://github.com/alibaba-damo-academy/RynnEC