# Action Chunking (ACT) Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/action-chunking-act
> Markdown URL: https://aitinkerers.org/technologies/action-chunking-act.md
> Technology record last updated: 2026-06-03T09:02:07Z
> Generated: 2026-09-23T02:27:24Z

Action Chunking with Transformers (ACT) is an imitation learning algorithm that enables low-cost robots to perform precise, fine-grained manipulation tasks by predicting sequences of future actions rather than single steps.

Developed by researchers at Stanford and Google, Action Chunking with Transformers (ACT) solves the compounding error problem in robotic imitation learning (1.1.4, 1.2.9). Instead of predicting one action at a time, ACT uses a Conditional Variational Autoencoder (CVAE) and a Transformer architecture to output a continuous chunk of future actions (1.1.4, 1.1.9). This approach allows low-cost, imprecise hardware to master highly complex, contact-rich tasks (such as opening condiment cups, slotting batteries, or folding clothes) with 80% to 90% success rates using only 10 minutes of human demonstration data (1.2.2, 1.2.4, 1.2.9).

- Official technology site: https://tonyzhaozh.github.io/aloha/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Teaching a robot to always win connect four via LeRobot](https://berlin.aitinkerers.org/talks/rsvp_HveawmqfJoQ)

I trained an autonomous robot arm setup using Hugging Face’s LeRobot framework that plays a game of Connect Four and is annoying as it always beats human opponents. The robot perceives the physical board state via a camera, calculates the optimal move, and executes the physical token drop.

- Event context: AI Tinkerers Berlin - June 17, 2026 — 2026-06-17 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_HveawmqfJoQ

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