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Paxini Gen3: From Sensing to Tactile-Enhanced Robot Learning

The complete Paxini Gen3 learning path. In ~8 hours go from first sensor reading to training and deploying a tactile-aware robot manipulation policy. Intermediate, hardware-first.

In ~8 hours, go from your first sensor reading to a trained robot manipulation policy that uses tactile feedback. You will build a complete grasp quality detection pipeline integrated with robot arm data collection — the same workflow used in contact-rich manipulation research.

Total Time ~8 hours

Difficulty Intermediate

Units 6 (+ Orientation)

Hardware Required Paxini Gen3 + any robot arm

Prerequisites Basic Python, a robot arm

Before you start this path: You should be comfortable writing basic Python scripts (reading data from a device, parsing arrays) and have a robot arm with a Python SDK you can control. You do not need prior robotics research experience. If you are new to robot data collection in general, complete the OpenArm learning path first — it covers data collection fundamentals in more depth.

What You Will Build

By the end of this path you will have:

Live Tactile Heatmap

A streaming pressure visualization running at 500 Hz from your Gen3 sensor, confirming every taxel is functional.

Grasp Quality Detector

An online classifier that distinguishes stable grasps from slip-prone ones in real time during robot operation.

Tactile Dataset (50 demos)

A full LeRobot-format dataset with synchronized tactile + joint + camera channels, quality-checked and ready for training.

Tactile-Aware Policy

A trained ACT or Diffusion Policy model that uses tactile input — evaluated against a vision-only baseline.

Path Overview

Complete units in order. Each unit ends with a concrete completion check so you know when to move on.

Start the Path — Unit 0 →

Or jump directly to Unit 1 if you have already done orientation.