# re1 · Good AI Labs

> Spatial memory for physical AI. Return to an observation, inspect its source video and reconstruct the place for simulation.

URL: https://www.goodailabs.com/products/re1/
Date: 2026-10-03
Status: In development. Technical previews available.

## What it does

- Revisit an observation: Retrieve video moments, verify source clips and see which parts of a recording were inspected.
- Reconstruct a place: Return to a captured environment, explore another viewpoint and inspect individual objects.
- Prepare a simulation: Carry captured environments into simulation workflows for further inspection and testing.

re1 is the spatial memory system we are building for physical AI. It keeps
observations available after the original session: a query can return to a moment,
a researcher can inspect the source, and a reconstruction can be reused for
simulation.

Recorded experience should remain useful as an experiment changes. A new question
may call for an earlier observation; a new trial may need the same environment.
re1 brings those records and places back into reach.

Inspect the reconstructed place. A recorded flight through a completed scene in the re1 viewer. The camera moves from an overhead view to street level.
The reveal is a visualization of a completed reconstruction, not its processing time.
Recording or figure: https://www.goodailabs.com/media/re1/reconstruction.mp4

## A memory that keeps its sources

Search a recording. Check the answer. Revisit the place.

Captured in the re1 research preview.

1. Find the observation you need.
A search for machines handling packages indoors returns warehouse and robot-arm recordings. Open a match to inspect the source.
Figure: Actual search results. Open the image to inspect the original interface.
Capture: https://www.goodailabs.com/media/re1/search-results.webp

2. Follow an answer back to its source.
The captured answer cites 0:12–0:20 of the warehouse recording. The reader can open that interval and check the description against the video.
Figure: Excerpt from a recorded response. Its citation covers the stated interval.

3. Keep a place available after capture.
Move through a captured environment from a new viewpoint and inspect its objects. Prepared scenes remain available for later work.
Figure: A frame from the completed reconstruction shown in the video on this page.
Capture: https://www.goodailabs.com/media/re1/reconstruction-poster.webp

Describe an event, find a matching recording and inspect what happened. Questions
about motion and sequence can lead back to the relevant source video, with time
references that make the answer possible to check.

The preview below searches for machines handling packages, opens a matching clip
and asks a question about it. The answer identifies the interval it describes.
A checked interval supports a claim about that interval; it does not establish
what happened throughout the recording.

A query becomes a set of inspectable clips. For “machines handling packages indoors,” the captured search returns the warehouse and robot-arm clips. Unconfirmed candidates remain visible below the matches.
This capture shows frame verification at the search stage; the question workflow checks source video intervals.
Recording or figure: https://www.goodailabs.com/media/re1/search-results.webp

Follow the answer back to the recording. A captured response links its description to 0:12–0:20 of the warehouse video. It describes that interval; it does not establish safety across the recording.

## From capture to a simulated scene

Return to a captured place and inspect it from a different viewpoint. The scene
remains available after the recording session, with objects that can be examined
individually and environment artifacts that can be carried into simulation work.

The recording above moves through a completed scene. Its visible gaps and incomplete
regions are part of the result. Playback shows the viewing experience, not the
time needed to prepare the environment.

Using a scene for physical experiments requires more than a convincing image.
Scale, contact behavior and material properties need validation for the task.

## Work that carries forward

Recordings and prepared scenes remain available across sessions. Researchers can
return to earlier evidence or reuse an environment as their experiment develops.
The aim is to reduce repeated preparation and make prior observations easier to use.

Resource requirements depend on the capture and the work requested. We have not
published system-wide latency or hardware requirements.

## Research direction

Our longer-term aim is memory that improves how an agent learns from experience.
The current preview covers video recall and captured environments. Connecting this
memory to policy learning remains an active research direction.

For a technical preview or research collaboration, send a representative capture,
the question you need to answer and the artifact your experiment needs to consume.
We are particularly interested in video memory, scene reconstruction and
robot-learning workflows.

[Contact the
lab](mailto:research@goodailabs.com?subject=re1%20technical%20preview).

Contact: research@goodailabs.com