---
title: "Astribot’s CoMind cuts robot communication traffic by 80% in dual-task trials"
slug: "astribots-comind-cuts-robot-communication-traffic-by-80-in-dual-task-t"
date: "2026-11-13"
category: "AI & Automation"
author: "Dr. Kai Wei"
correspondent: "Dr. Kai Wei"
correspondentRole: "Lead Frontier AI Correspondent"
readingTimeMinutes: 2
tags: ["AI","News"]
originalSourceUrl: "https://pandaily.com/astribot-comind-be-my-eye-multi-robot-collaboration-active-context-sharing"
canonicalUrl: "https://aurav.ae/astribots-comind-cuts-robot-communication-traffic-by-80-in-dual-task-t"
publisher: "Aura-V Intelligence (aurav.ae)"
---

# Astribot’s CoMind cuts robot communication traffic by 80% in dual-task trials

> Astribot’s CoMind framework lets two robots share compressed, task-relevant visual data, boosting success rates while slashing bandwidth.

## Key Takeaways (TL;DR)

- CoMind compresses multi-camera inputs into compact representations for robot-to-robot exchange.
- Success on a partitioned pen task rose from 0% to 100% with roughly 80% less traffic.
- Blind-box selection accuracy improved to 88.9% versus 38.9% without sharing.

Astribot announced that its CoMind (also called Be My Eye) framework enables two robots to exchange compact, task-relevant visual contexts instead of raw video streams, reducing communication volume by about 80% [Pandaily AI & Tech China](https://pandaily.com/astribot-comind-be-my-eye-multi-robot-collaboration-active-context-sharing). The system addresses partial observability by having each robot encode its multi-camera view and instruction into a distilled representation, which the partner fuses with its own sensors before feeding a pretrained vision-language-action model for action generation.

In live tests on tasks such as restocking a fridge, assembling a carton, and selecting a marker pen across a partition, the framework delivered marked performance gains. On the partitioned pen task, success climbed from 0% without sharing to 100% with CoMind. Blind-box selection accuracy rose from 38.9% (no sharing) to 88.9% with CoMind, surpassing a competing context-transfer method that achieved 72.2%.

Astribot, founded in late 2022 and led by CEO Lai Jie, positions CoMind as a complement to higher-level task planning and plans to extend the approach to richer shared information and more flexible multi-robot interactions. Results are based on the company’s internal tests and have not been independently verified.

---

*Original Think-Tank Source: [Astribot's CoMind Lets Robots Lend Each Other Their Eyes, Cutting Shared Data by About 80%](https://pandaily.com/astribot-comind-be-my-eye-multi-robot-collaboration-active-context-sharing)*