We’ve been working on Telekinesis, a developer SDK aimed at reducing fragmentation in robotics, computer vision, and Physical AI.
This started from a recurring problem we ran into while building real systems: even relatively simple robotics applications often require stitching together a large number of incompatible components — classical robotics libraries, perception pipelines, learned models, and increasingly foundation models like LLMs and VLMs.
The problem we’re trying to address
Robotics software development is highly fragmented:
Perception, planning, control, and learning often live in separate ecosystems
Each library comes with its own APIs, assumptions, and data formats
Integration glue ends up dominating development time
Mixing classical robotics with modern AI workflows is still painful
As Physical AI and agent-based systems become more common, this gap between classical robotics workflows and modern AI tooling is becoming more acute.
What Telekinesis is
Telekinesis is a large-scale skill library for Physical AI, exposed through a single, consistent Python interface.
Instead of being another robotics framework, it’s designed as a modular, composable set of skills that can be combined into complete systems.
The SDK currently covers:
3D perception: detection, registration, filtering, clustering
The SDK will also cover:
2D perception: image processing, detection, segmentation
Synthetic data generation
Model training tools
Motion planning, kinematics, and control
Physical AI agents
Vision–Language Models (VLMs)
The idea is that roboticists and Computer Vision engineers can access these capabilities without spending most of their time integrating fragmented libraries, and instead focus on system behavior and iteration.
How it’s structured
From the developer’s perspective, Telekinesis provides:
A single Python interface
Hundreds of modular, composable skills
The ability to combine perception, planning, control, and AI components predictably
The skills are hosted on the cloud by default, but the same architecture can also be run on-premise for teams that need full control over data and computation.
This makes it possible to:
Prototype quickly
Reuse components across projects
Scale from experiments to more industry-grade systems without changing APIs
Who this is for
The SDK is intended for:
Robotics engineers working close to perception or control
Computer vision developers building systems, not just models
People experimenting with Physical AI and embodied agents
In particular, for those who feel they spend more time integrating components than evaluating system behavior.
What we’re still figuring out
This is still early, and we’re actively questioning parts of the design:
Where abstractions help vs. hide too much
Which components should never be unified
How to balance flexibility with predictability
How this compares to existing robotics + ML workflows in practice
Happy to hear critical perspectives from people who’ve built or maintained real systems.
Here the documentation (still evolving): https://docs.telekinesis.ai/
Thanks for reading.