An open source approach to physical AI from Intrinsic

An open source approach to physical AI from Intrinsic

A repeating pattern of a photograph of a silicon chip, recoloured so that it is multi-coloured, in the style of pop art.Deborah Lupton / Pop Chips / Licenced by CC-BY 4.0

Intrinsic (an AI robotics group at Google) announced that the company is making parts of its platform open source. Intrinsic Core™ is a set of ROS-compatible capabilities for building sophisticated robotic applications.

The capabilities included in the open source package are as follow:

  • Intrinsic Control. A hardware-agnostic, real-time control framework that delivers sensor-based control. It allows the robot to dynamically adapt its behavior mid-trajectory based on changes in the environment. It is designed to deliver efficient, collision-free paths. The idea is that users can swap out different robot arms, grippers, sensors, and more without needing to rewrite drivers.
  • Pose estimation. Provides an accurate six degrees of freedom pose estimation of 3D parts.
  • Motion planning. Logic auto-generates efficient paths while avoiding collisions.
  • Grasp planning. Designed to enable various grippers to automatically and dynamically adapt to the location and orientation of a part and grasp it with a higher probability of success.
  • Simulation services. These aim to provide a straightforward way to test and troubleshoot robotic solutions that update as you build.
  • Camera calibration. Automates how a robot’s camera can be calibrated and physically aligned
  • Intrinsic-ROS drivers. Pre-configured, ROS-compatible drivers for supported robots, grippers, and 3D cameras.

Alongside this package, Intrinsic has also released Open Machine Tending Solution (OMTS). This is a ready-made reference design for a real-world use case. The intention is to give developers a starting point to customize their own solution, without needing to build from scratch.

You can download Intrinsic Core on GitHub here.

Comments are closed.