In this post, we dive into the architecture of the Telekinesis Physical AI stack.

docs.telekinesis.ai

In our previous post, “Creating an Agentic Skill Library for Robotics, Computer Vision, and Physical AI”, we presented our core thesis that Vision-Language-Action (VLA) models, much like Large Language Models (LLMs), will inevitably be commoditized.

Link - (https://medium.com/@telekinesis-ai/creating-an-agentic-skill...)

When that happens, scaling Physical AI beyond polished demos and into practical, real-world robotics and industrial computer vision applications will require a different approach, namely, an Agentic Physical AI stack.

This article zooms out to map the Telekinesis landscape and explain the architecture that brings perception, learning, motion planning, and reasoning together into a coherent ecosystem.

To learn more, here are some resources to get started:

Documentation to the Telekinesis Developer SDK: https://docs.telekinesis.ai/

Github examples: https://github.com/telekinesis-ai/telekinesis-examples

Join the Telekinesis Robotics Community

We’re building a community of developers, researchers, and robotics enthusiasts who want to help grow the Telekinesis Skill Library. If you’re working on robotics, computer vision, or Physical AI and have a Skill you’d like to share or contribute, we’d love to collaborate.

Join the conversation on our Discord community, share ideas, and help shape the future of agentic Physical AI: https://discord.gg/cxTMdkMs