# Xiaomi reports apartment trial of its robot model

> A reported multiroom run adds a practical setting to earlier research, while leaving reliability and operating conditions open.

By BIGCHANGE.AI Editorial

Published: 2026-11-03T14:08:38.375Z
Updated: 2026-11-03T14:08:38.375Z
Canonical: https://bigchange.ai/blog/xiaomi-robot-model-apartment-trial

![Conceptual charcoal illustration of a white humanoid robot walking toward a closed drawer in a light wood kitchen.](https://bigchange.ai/api/media/file/homebody-kitchen-g1-hero-v1.png)
AI-generated conceptual illustration by BIG CHANGE.

IT Home reports a Xiaomi robot trial in a company apartment.

## The big change

- The reported run spans multiple rooms and household tasks.
- Earlier research trained the model on manipulation data and aligned actions with different robot bodies and instructions.
- The trial report does not establish repeat success or intervention rates.

## From a reported run to household work

On November 3, IT Home reported Xiaomi’s video describing a system based on Xiaomi-Robotics-1 in trial operation at a company youth apartment. The publication relayed a run of more than 30 minutes across three rooms and over 50 tasks, including tidying surfaces and collecting rubbish.

The report describes a loop joining data collection, model inference and task execution. It does not provide the number of attempts, interventions or a repeated-run success rate. BIG CHANGE could not independently access the underlying video.

## How the model was trained and tested

Xiaomi’s separate July paper describes a vision-language-action model trained on real-world manipulation recordings. Post-training aligns its learned actions with different robot bodies and language instructions.

In one earlier evaluation, researchers tested four familiar task types with unseen objects and environments, without task-specific or environment-specific fine-tuning. For its five-billion-parameter comparison, Xiaomi reported 75% overall success after action pre-training on 20,000 hours of recordings, versus 26% without action pre-training. Both used identical post-training. Four familiar task types were tested with unseen objects and environments. That supports a pre-training benefit under those test conditions.

A separate evaluation used four tasks held out from the in-house robot data, then fine-tuned on those tasks and tested each model ten times per task.

## A separate household research system

Stanford and Caltech’s HomeBody uses persistent spatial memory and a library of reusable skills to organize longer household tasks. A vision-language model calls skills such as navigation and grasping and receives feedback on their execution. BIG CHANGE previously covered [that separate project](https://bigchange.ai/blog/homebody-humanoid-spatial-memory-robot-skills).

## Sources

- [IT Home: Xiaomi apartment trial](https://www.ithome.com/1/021/602.htm) — IT Home reports Xiaomi's November 3 video and trial claims; the video was not independently accessed.
- [Xiaomi Robotics-1 research paper](https://arxiv.org/html/2607.15330v2) — Xiaomi's July paper explains model training and bounded experiments.
- [Stanford and Caltech: HomeBody](https://tml.stanford.edu/homebody/) — Stanford and Caltech describe their household robot research system.