Telekinesis Agentic Skill Library for Computer Vision, Robotics and Physical AI ​

A large-scale Python skill library for building Computer Vision, Robotics and Physical AI systems.

Skill Library eliminates glue code between computer vision, planning, control, and learning.
Train in simulation. Orchestrate with AI agents. Deploy to real hardware — using the same Skill Groups.

We provide:

• Skills: a broad set of AI algorithms for perception, motion planning, and control.
• Physical AI Agents: LLM/VLM agents for task planning across industrial, mobile, and humanoid robots.

Get started immediately with the Telekinesis Agentic Skill Library (Python 3.11 or 3.12):

pip install telekinesis-ai

Industrial Applications ​

Telekinesis Agentic Skill Library helps you build real-world robotics and Physical AI applications for industries such as manufacturing, automotive, aerospace and others. We present some use cases in manufacturing which the Telekinesis team has already deployed using the skill library.

Simulation Applications ​

Develop and simulate digital twin workflows to validate, stress-test, and optimize Skill Groups. Deploy the same Skill Groups to real-world robots using a simulation-to-real transfer pipeline.

Computer Vision Skills ​

Use the production-grade computer vision Skill Groups for obstacle detection, ground navigation, pose estimation, point cloud processing, bin picking, conveyor systems and AI model training.

3D Object Detection & 6D Pose Estimation

Mesh Processing

3D Point Cloud Registration

Obstacle Detection

Human Identification

Ground Navigation

Bin Picking

Depallitizing Boxes

PCB Segmentation

Conveyor Tracking

Object Counting

Parts Inspection

Skills can be imported like shown below:

from telekinesis import cornea    # image segmentation skills
from telekinesis import retina    # object detection skills
from telekinesis import pupil     # image processing skills
from telekinesis import vitreous  # point cloud processing skills
from telekinesis import iris      # AI model training skills

Furthermore, we offer medulla which is a unified interface to cameras such as: Zivid, Mechmind, Microsoft Kinect, and others.

from telekinesis import medulla   # sensor interface skills

Learn more in the Medulla Overview.

Synthetic Dataset Generation Skills ​

Generate photo-realistic synthetic datasets to train and validate computer vision models. Replace months of manual data collection with our Illusion module.

Synthetic Data 1

Synthetic Data 2

Synthetic Data 3

Synthetic Data 4

Synthetic Data 5

Synthetic Data 6

Synthetic Data 7

Synthetic Data 8

Synthetic Data 9

Easily call the skill like shown below:

from telekinesis import illusion   # synthetic data generation skills

Find out more about the synthetic dataset generation skills in Illusion Overview.

Robotics Skills ​

Using the skill group called neuroplan, prototype on any robot(industrial, mobile, or humanoid robot), perform any task on the same platform, and deploy the same Skill Groups anywhere - any robot, any task, on one Physical AI platform.

One of the biggest pains of robotics is that each robot provider has their own interface to control their robots.

Use our library to run the same Skill Groups to interact with the leading industrial and mobile robots: Universal Robots (real & simulation), KUKA (real & simulation), ABB (real & simulation), Franka Emika (real & simulation), Boston Dynamics (simulation), Anybotics (simulation), Unitree (simulation).

Import robotic skills like shown below:

from telekinesis import neuroplan # robotics skills

Explore the full Neuroplan robotics stack in Neuroplan Overview.

Reinforcement Learning Skills ​

Train and deploy reinforcement learning (RL) policies for robotics with the Telekinesis Agentic Skill Library. From simulation to real hardware, RLBotics brings learned behaviors—locomotion, manipulation, and control—into your Physical AI pipelines.

Access the skills like shown below:

from telekinesis import rlbotics   # reinforcement learning skills

Learn about all the ways to train your reinforcement learning policies in RLBotics Overview.

Who Is Telekinesis For? ​

• Robotics startups & product teams
  Ship faster by using production-grade perception, motion planning, control, and reinforcement learning — without stitching together fragmented stacks.

• Industrial automation engineers & integrators
  Deploy sim-to-real robotic systems with a unified Python SDK that works across industrial, mobile, and humanoid robots.

• AI & computer vision researchers
  Prototype, validate, and deploy Physical AI pipelines — from 6D pose estimation to reinforcement learning policies — in one consistent framework.

Join our Discord community to exchange ideas, contribute Skills, and accelerate the development of real-world robotics systems.

What is a Skill? ​

A Skill is a reusable operation for robotics, computer vision, and Physical AI. Skills span 2D/3D perception (6D pose estimation, 2D/3D detection, segmentation, and image processing), motion planning (RRT*, motion generators, trajectory optimization), and motion control (model predictive control, reinforcement learning policies). Skills can be chained into pipelines to build real-world robotics applications.

Below are examples of what a Skill looks like:

Example 1: segment_image_using_sam. This skill performs segmentation on an image using SAM model.

# Example 1
from telekinesis import cornea                                # Import Cornea - Image segmentation module

# Executing a 2D image segmentation Skill                     
result = cornea.segment_image_using_sam(                      # Executing Skill - `segment_image_using_sam`
    image=image,
    bboxes=[[400, 150, 1200, 450]]
)
# Access results
annotations = result.to_list()

Example 2: detect_objects_using_rfdetr. This skill performs object detection on image using RFDETR.

# Example 2
from telekinesis import retina                                   # Import Retina - Object detection module

# Executing a 2D object detection Skill
annotations, categories = retina.detect_objects_using_rfdetr(    # Executing Skill - `detect_objects_using_rfdetr`
    image=image,
    score_threshold=0.5,
)
# Access results
annotations = annotations.to_list()
categories = categories.to_list()

Skills are organized in Skill Groups:

from telekinesis import cornea

from telekinesis import retina

from telekinesis import pupil

from telekinesis import vitreous

from telekinesis import illusion

from telekinesis import iris

from telekinesis import neuroplan

from telekinesis import cortex

from telekinesis import rlbotics

What is a Physical AI Agent? ​

Recent advances in LLMs and VLMs, including systems such as LLama 4, Mistral, Qwen, Gemini Robotics, RT-2, π₀, world models, and Dream-based VLAs have shown the potential of learned models to perform semantic reasoning, task decomposition, and high-level planning from vision and language inputs.

In the Telekinesis library, a Physical AI Agent, typically a Vision Language Model (VLM) or Large Language Model (LLM), autonomously interprets natural language instructions and generates high-level Skill plans. In autonomous Physical AI systems, Agents continuously produce and execute Skill plans, allowing the system to operate with minimal human intervention.

To learn more about building the Telekinesis Physical AI Agents, explore Cortex.

How do Physical AI Agents use Skills to Perform Tasks? ​

Telekinesis Agentic Skill Library Architecture

Flow Overview

1. A user prompts the robot with a task: "Hey, can you do a CNC machine loading task?"
2. The Agent interprets the intent and reasons over the given task and sequencing.
3. The Agent constructs a high-level plan and orchestrates the Skills — selecting order and parameters.
4. Skills are executed on the robot using production-grade perception, motion, and control algorithms, with continuous feedback from the environment.

To understand the architectural motivations and system design of Telekinesis, read the Introduction.

Developer Quickstart ​

You can easily integrate Telekinesis Agentic Skill Library into your own application. Setup the library in just 4 steps and start building!

Step 1: Generate an API Key ​

Since all the skills are hosted on the cloud, to access them securely, a free API key is needed. Create a Telekinesis account and generate an API key for free: Create a Telekinesis account!

Store the key in a safe location, such as your shell configuration file (e.g. .zshrc, .bashrc) or another secure location on your computer.

Step 2: Configure the API Key ​

Export the API key as an environment variable. Open a terminal window and run below command as per your OS system.

Replace <your_api_key> with the one generated in Step 1.

export TELEKINESIS_API_KEY="<your_api_key>"

setx TELEKINESIS_API_KEY "<your_api_key>"

The Telekinesis SDK uses this API key to authenticate requests and automatically reads it from your system environment.

Step 3: Pip Install the Telekinesis Agentic Skill Library ​

We currently support Python versions - 3.11, 3.12. Ensure your environment is in the specified Python version.

Install the core SDK using pip:

pip install telekinesis-ai

Step 4: Run Your First Example ​

1. Git clone our telekinesis-examples repository from Github with:

git clone --depth 1 --recurse-submodules --shallow-submodules https://github.com/telekinesis-ai/telekinesis-examples.git

2. Change directory into telekinesis-examples:

cd telekinesis-examples

3. Install example-only dependencies:

pip install numpy scipy opencv-python rerun-sdk==0.27.3 loguru pycocotools

4. Run the segment_image_using_sam example:

python examples/cornea_examples.py --example segment_image_using_sam

If the example runs successfully, a Rerun visualization window will open showing the result.

Input-Output Visualization

Join our Discord Community to Add your Own Skills ​

If you zoom out, the Telekinesis Agentic Skill Library is just the beginning.

We want you to join us. Maybe you’re a researcher who just published a paper and built some code you’re proud of. Maybe you’re a hobbyist tinkering with robots in your garage. Maybe you’re an engineer tackling tough automation challenges every day. Whatever your background, if you have a Skill, whether it’s a perception module, a motion planner, or a clever robot controller, we want to see it.

The idea is simple: release your Skill, let others use it, improve it, and see it deployed in real-world systems. Your work could go from a lab or workshop into factories, helping robots do things that were previously too dangerous, repetitive, or precise for humans.

Our vision is about building something practical, reusable, and meaningful. Together, we can make robotics software accessible, scalable, and trustworthy. And we’d love for you to be part of it.

Join our Discord community to be part of the Physical AI revolution!

What Next? ​

Explore all the features of Telekinesis Agentic Skill Library with Cornea!