Eximius Labs

Eximius Labs builds open-weight multimodal models that run on your own hardware: the embedding, perception, and memory layers for physical AI. The technical report is on arXiv: https://arxiv.org/abs/2607.18666. Try a real robot's memory in the live playground: https://www.eximiuslabs.com/playground.

We freeze a strong base model and add new senses from the outside, so existing embeddings stay valid and gaining a modality never forces a re-index. One shared vector space spans text, image, video, audio, thermal, motion (IMU), and touch.

Fusion Embedding, the embedding layer

One vector space for cross-modal retrieval, RAG, clustering, and semantic search.

• fusion-embedding-2 — text, image, video, and audio in one space (connector plus modality-gated adapters).
• fusion-embedding-1 — the connector-only first generation.
• Ember — thermal and infrared, as a loadable sense pack.
• Tremor — body-worn and robot motion (IMU and accelerometer), read as language, with a Unitree-G1 head.
• Tactus — touch (32x32 pressure/taxel arrays from FSR gloves, e-skins, and robot hands), as a loadable sense pack; matches to exceeds the STAG (Nature 2019) supervised baseline while remaining open-vocabulary.
• fusion-embedding-2-k3-vision — Kimi K3's vision encoder, projected into the shared space.

Fusion Perception, the perception layer

Dense scene understanding and geometric place recognition on a frozen vision backbone, projected into the Fusion Embedding space. Code: https://github.com/Eximius-Labs/fusion-perception.

Engram, the memory layer

The open cross-modal memory layer for physical AI, built on Fusion Embedding. It indexes a robot's video, audio, and motion into one embedding space on a shared clock and answers questions about it in plain language, including temporal reasoning that retrieval alone cannot do.

pip install engram-robomem

Code: https://github.com/Eximius-Labs/engram · PyPI: https://pypi.org/project/engram-robomem · Playground: https://www.eximiuslabs.com/playground

Build with it

• Site: https://www.eximiuslabs.com
• Cookbook: https://github.com/Eximius-Labs/cookbook
• Code: https://github.com/Eximius-Labs
• Paper: https://arxiv.org/abs/2607.18666

Everything is open weight and self-hostable. Core models are research preview; the sensor packs are CC-BY-NC-4.0.