act-aloha

Automate bimanual cube transfer using vision-language-action policies.

Updated Jul 5, 2026
One-click install
npx skills add https://github.com/bensonlee5/openral --skill act-aloha
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: act-aloha
Source: https://github.com/bensonlee5/openral/tree/main/rskills/act-aloha
Command: npx skills add https://github.com/bensonlee5/openral --skill act-aloha

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex task of transferring cubes using bimanual robots, leveraging vision-language-action policies to streamline operations.

Core Features & Use Cases

  • Vision-Language-Action Policy: Utilizes an Action Chunking Transformer to interpret visual input and execute precise actions.
  • Bimanual Robot Support: Designed for robots with two 7-DoF arms, enabling complex manipulation tasks.
  • Simulation-Ready: Ready for use in simulations for training and testing purposes.
  • Use Case: Ideal for robotic assembly lines or research into bimanual manipulation.

Quick Start

Use the act-aloha skill to transfer a cube from one position to another on the ALOHA bimanual robot.

Frequently Asked Questions about act-aloha

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate bimanual cube transfer tasks using vision-language-action policies?

Bimanual cube transfer tasks are automated using vision-language-action policies that leverage an Action Chunking Transformer to interpret visual input and execute precise robotic manipulation actions.

What is a vision-language-action policy for bimanual manipulation?

A vision-language-action policy for bimanual manipulation uses an Action Chunking Transformer to process visual input and generate precise actions for robots with two 7-DoF arms to transfer cubes.

Do I need a specific simulation environment to train bimanual manipulation policies?

You need a suitable simulation environment and OpenRAL to train and execute bimanual manipulation policies, enabling safe testing of vision-language-action tasks before physical deployment.

How does the Action Chunking Transformer work for robotic assembly automation?

The Action Chunking Transformer works for robotic assembly automation by interpreting visual input and translating it into precise, chunked actions for bimanual robots handling cube transfers.

Can I use this vision-language-action approach for robots without two 7-DoF arms?

This vision-language-action approach is specifically designed for robots with two 7-DoF arms, making it unsuitable for robotic platforms that do not support bimanual manipulation configurations.

What is the best way to train bimanual robots for assembly line cube transfers?

The best way to train bimanual robots for assembly line cube transfers is by using simulation-ready vision-language-action policies to streamline the training and testing of complex manipulation operations.