telekinesis-ai 0.0.1rc5

Official Telekinesis SDK for working with Telekinesis APIs.

Telekinesis Examples | Telekinesis Data

Telekinesis SDK

Telekinesis SDK is a modular Python-based SDK for Physical AI, providing a unified set of algorithms for robotics, 3D perception, computer vision, motion planning, and vision-language models.

It is designed for roboticists and computer vision engineers who want to build end-to-end Physical AI systems without stitching together fragmented libraries.

What You Can Build With Telekinesis SDK

Telekinesis SDK includes:

• 3D perception (filtering, registration, clustering)
• 2D perception (image processing, segmentation)
• Synthetic data generation
• Motion planning, kinematics, and control
• Vision-Language Models (VLMs)
• Physical AI agents

Learn more about the Telekinesis SDK in the About Telekinesis.

Release Model

Telekinesis SDK is currently in its initial release cycle, published as release candidates (RC).

It is to be noted that in this phase, SDK modules are introduced incrementally.

Currently available modules:

• vitreous
• pupil

Installation

Telekinesis SDK supports Python 3.11 and 3.12.

Install the SDK using pip:

pip install telekinesis-ai

Getting Started

Telekinesis SDK requires a valid API key to authenticate requests.

If you have not yet set up your API key, follow the official Quickstart Guide to set up your API key.

Example

The following example assumes the API key has been generated and has been set as TELEKINESIS_API_KEY environment variable.

Run a python code to quickly test your installation:

This example will fail if TELEKINESIS_API_KEY is not set correctly.

import numpy as np
from telekinesis import vitreous

# Create a cylinder mesh
cylinder_mesh = vitreous.create_cylinder_mesh(
		radius=0.01,
		height=0.02,
		radial_resolution=20,
		height_resolution=4,
		retain_base=False,
		vertex_tolerance=1e-6,
		transformation_matrix=np.eye(4, dtype=np.float32),
		compute_vertex_normals=True,
	)

# Convert it to point cloud
point_cloud = vitreous.convert_mesh_to_point_cloud(
		mesh=cylinder_mesh,
		num_points=10000,
		sampling_method="poisson_disk",
		initial_sampling_factor=5,
		initial_point_cloud=None,
		use_triangle_normal=False,
	)
print(point_cloud.positions)
# Use point_cloud in downstream processing or visualize the point cloud with any tool        

Expected output: Some logs and random valued point cloud positions in the below format is output

...
... 
[[-0.00835031 -0.00536731 -0.00429686]
 [ 0.00854885  0.00497764  0.00044501]
 [ 0.00838172  0.00530565  0.00249433]
 ...
 [-0.00280485  0.00955575  0.00949276]
 [-0.00743726 -0.00653076 -0.00238814]
 [ 0.00023231 -0.00996321  0.00887559]]

You are now set up to build with Telekinesis.

The recommended way to explore Telekinesis SDK today is via the Telekinesis Examples repository, which contains fully runnable workflows built on top of the SDK.

Resources

• Examples
  Runnable examples demonstrating Telekinesis SDK capabilities: Telekinesis Examples

• Documentation
  Full SDK documentation and usage details: Telekinesis Docs

• Sample Data
  Datasets used across the examples: Telekinesis Data

Support

For issues and questions:

• Create an issue in the GitHub repository.
• Contact the Telekinesis development team.

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