Telekinesis Skill Examples

Free Tier

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Create API key →

Prerequisites

New here? Complete the Quickstart first — it sets up your API key, conda environment, and SDK in under 5 minutes.

Run an Example!

Run any example from telekinesis-examples repository

Clone the repository and the telekinesis-data submodule

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

cd telekinesis-examples

What gets downloaded?

The flags above keep the clone small:

• --depth 1 fetches only the latest commit, not full history.
• --recurse-submodules --shallow-submodules also pulls the telekinesis-data submodule (sample images, point clouds, robot states), again at depth 1. Roughly 1.5 GB of free disk space is required for the telekinesis-data.

When you start using Telekinesis on your own data, you can ignore the telekinesis-data submodule - it exists purely to make the examples runnable out of the box.

Install dependencies for examples

conda activate telekinesis
pip install rerun-sdk==0.31.2 pycocotools scipy

Run an example (see more examples below)

Runs segment_image_using_sam and visualizes masks in Rerun.

python examples/cornea_examples.py --example segment_image_using_sam

SAM masks predicted from the sample input image.

Cornea

List all available examples and run one:

python examples/cornea_examples.py --list

python examples/cornea_examples.py --example <name>

Cornea

Segment Image Using RGB

Segments regions by thresholding raw RGB color channels.

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Cornea

Segment Image Using HSV

Segments regions by thresholding hue, saturation, and value.

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Cornea

Segment Image Using LAB

Segments regions using perceptually uniform LAB color space.

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Cornea

Segment Image Using YCrCb

Segments regions by separating luminance from chroma components.

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Cornea

Segment Image Using Focus Region

Isolates a rectangular region of interest for downstream processing.

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Cornea

Segment Image Using Watershed

Segments touching or overlapping objects using the watershed algorithm.

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Cornea

Segment Image Using Flood Fill

Segments a contiguous region by flood-filling from a seed point.

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Cornea

Segment Image Using Foreground BiRefNet

Extracts foreground from background using the BiRefNet deep learning model.

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Cornea

Segment Image Using SAM

Segments arbitrary objects using Meta's Segment Anything Model (SAM).

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Cornea

Segment Image Using GrabCut

Separates foreground from background using the GrabCut graph-cut algorithm.

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Cornea

Segment Image Using Felzenszwalb

Over-segments an image into superpixels using Felzenszwalb's method.

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Cornea

Segment Image Using SLIC Superpixel

Groups pixels into compact superpixels using the SLIC algorithm.

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Cornea

Filter Segments By Area

Removes segments outside a specified minimum or maximum area.

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Cornea

Filter Segments By Color

Keeps only segments whose average color falls within a target range.

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Cornea

Filter Segments By Mask

Filters segments based on overlap with a provided binary mask.

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Cornea

Segment Image Using Otsu Threshold

Binarizes an image by automatically finding the optimal Otsu threshold.

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Cornea

Segment Image Using Local Threshold

Applies adaptive thresholding over local image neighborhoods.

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Cornea

Segment Image Using Yen Threshold

Binarizes an image using Yen's maximum correlation thresholding method.

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Cornea

Segment Image Using Threshold

Segments an image using a fixed manually specified threshold value.

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Cornea

Segment Image Using Adaptive Threshold

Computes per-pixel thresholds based on local mean or Gaussian-weighted neighborhood.

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Cornea

Segment Image Using Laplacian Threshold

Detects and segments edges by thresholding the Laplacian of the image.

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python examples/cornea_examples.py --example segment_image_using_rgb

Retina

List all available examples and run one:

python examples/retina_examples.py --list

python examples/retina_examples.py --example <name>

Retina

Detect Circle Using Classic Hough

Detects circles in an image using the classical Hough Circle Transform.

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Retina

Detect Contours

Finds and draws object contours using edge-based detection.

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Retina

Detect Objects Using YOLOX

Detects objects using the YOLOX neural network model.

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Retina

Detect Objects Using RF-DETR

Detects objects using the RF-DETR transformer-based detection model.

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Retina

Detect Objects Using QWEN

Detects and describes objects using the Qwen vision-language model.

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Retina

Detect Objects Using Grounding DINO

Detects objects from free-text prompts using Grounding DINO.

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python examples/retina_examples.py --example detect_contours

Pupil

List all available examples and run one:

python examples/pupil_examples.py --list

python examples/pupil_examples.py --example <name>

Pupil

Bitwise AND Images

Perform a pixel-wise bitwise AND operation between two images or binary masks, useful for mask intersection and logical filtering.

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Pupil

Bitwise Difference Images

Compute the pixel-wise absolute difference between two images to highlight changes, useful for motion detection, defect inspection, and image comparison.

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Pupil

Bitwise NOT Image

Invert an image or binary mask by performing a pixel-wise bitwise NOT operation. Useful for mask inversion, foreground/background swapping, and logical complement operations.

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Pupil

Bitwise OR Images

Combine two images or binary masks using pixel-wise bitwise OR operation. Useful for mask union, merging detection results, and filling gaps.

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Pupil

Bitwise XOR Images

Compute pixel-wise exclusive regions between two binary images using bitwise XOR. Useful for change detection, mask comparison, and highlighting differences.

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Pupil

Calculate Mask Centroid

Compute the centroid (center of mass) of non-zero pixels in a binary mask. Useful for object localization, alignment, and tracking.

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Pupil

Calculate Mask PCA

Perform principal component analysis on a binary mask to compute centroid, eigenvectors, eigenvalues, and principal angle with visualization. Useful for shape orientation analysis, alignment, and morphological characterization.

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Pupil

Convert Image Color Space

Convert an image between color spaces (e.g., RGB, BGR, HSV, GRAY, RGBA) for segmentation, display, or cross-library compatibility.

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Pupil

Crop Image Center

Crop an image to specified dimensions centered on the image, with optional padding for smaller images. Useful for fixed-size model inputs, thumbnails, and normalization.

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Pupil

Crop Image Using Bounding Boxes

Crop an image using multiple rectangular bounding boxes, optionally retaining coordinate metadata. Useful for object detection, ROI extraction, and batch processing.

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Pupil

Crop Image Using Polygon

Crop an image using an arbitrary polygon mask, retaining pixels inside the polygon. Useful for irregular shapes, segmentation contours, and non-rectangular ROIs.

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Pupil

Enhance Image Using Auto Gamma Correction

Automatically adjust image brightness using adaptive gamma estimation to normalize exposure. Ideal for varying lighting conditions.

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Pupil

Enhance Image Using CLAHE

Apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance local contrast while controlling noise amplification, ideal for low-light or unevenly illuminated images.

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Pupil

Enhance Image Using White Balance

Apply automatic white balance correction to remove color casts and adjust color temperature, ensuring neutral colors appear natural under varying illumination.

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Pupil

Filter Image Using Bilateral

Apply a bilateral filter to reduce image noise while preserving edges by considering both spatial proximity and color similarity, ideal for edge-preserving smoothing in photography and vision pipelines.

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Pupil

Filter Image Using Blur

Apply a simple box blur to quickly smooth an image by averaging pixel values within a kernel. Ideal for fast preprocessing, background estimation, or artistic blur effects where edge preservation is not critical.

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Pupil

Filter Image Using Box

Apply a normalized box filter to perform basic averaging with control over kernel size, normalization, output depth, and border handling. Ideal for preprocessing, local averaging, and feature extraction where precision matters.

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Pupil

Filter Image Using Frangi

Apply the Frangi vesselness filter to enhance tubular structures in images, ideal for medical imaging, retinal scans, angiography, and other applications requiring vessel detection.

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Pupil

Filter Image Using Gabor

Apply a Gabor filter to detect oriented textures and features at specific scales and orientations, ideal for texture analysis, fingerprint recognition, and biomedical imaging.

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Pupil

Filter Image Using Gaussian Blur

Apply a Gaussian blur to smoothly reduce noise while preserving edges, ideal for preprocessing, image enhancement, and feature extraction in computer vision pipelines.

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Pupil

Filter Image Using Hessian Filter

Apply a Hessian-based vesselness filter to detect tubular and ridge-like structures, offering a faster alternative to Frangi for vessel detection and ridge enhancement in biomedical and industrial imaging.

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Pupil

Filter Image Using Laplacian Filter

Apply a Laplacian filter to detect edges and fine details using second-order derivatives, ideal for precise edge localization, image sharpening, and zero-crossing detection.

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Pupil

Filter Image Using Median Blur

Apply a median blur filter to remove salt-and-pepper noise while preserving edges, ideal for impulse noise removal and image restoration in vision pipelines.

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Pupil

Filter Image Using Meijering

Apply the Meijering filter to detect neurites and fine branching structures in biomedical images, ideal for neuronal imaging and morphological analysis.

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Pupil

Filter Image Using Morphological Blackhat

Apply morphological black-hat transform to extract small dark features, cracks, or holes on bright backgrounds, ideal for defect and surface inspection.

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Pupil

Filter Image Using Morphological Close

Apply morphological closing (dilation followed by erosion) to fill holes and connect nearby objects while preserving overall shape, ideal for segmentation cleanup and feature completion.

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Pupil

Filter Image Using Morphological Dilation

Apply morphological dilation to expand bright regions and fill small gaps or holes, useful for mask expansion, object connection, and feature enhancement.

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Pupil

Filter Image Using Morphological Erosion

Apply morphological erosion to shrink bright regions and remove small noise, useful for binary image cleanup, object separation, and feature removal.

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Pupil

Filter Image Using Morphological Gradient

Apply morphological gradient to extract object boundaries by computing the difference between dilation and erosion, useful for robust edge detection in binary or grayscale images.

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Pupil

Filter Image Using Morphological Open

Apply morphological opening (erosion followed by dilation) to remove small noise while preserving object size, effective for binary image cleanup and segmentation refinement.

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Pupil

Filter Image Using Morphological Tophat Filter

Apply morphological top-hat transform to extract or enhance small bright features in images, useful for background correction, small object detection, and detail enhancement.

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Pupil

Filter Image Using Sato Filter

Apply Sato filter for multi-scale ridge detection to enhance thin linear structures such as fibers, vessels, cracks, and PCB traces.

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Pupil

Filter Image Using Scharr

Apply Scharr filter for high-accuracy edge detection with improved rotation invariance, suitable for gradient-based applications.

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Pupil

Filter Image Using Sobel

Apply Sobel filter for directional edge detection, computing gradients in X and Y directions to detect edges and orientation.

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Pupil

Merge Image From Channels

Merge multiple single-channel images into a multi-channel image, useful for per-channel processing and color reconstruction.

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Pupil

Normalize Image Intensity

Normalize image intensity values using minmax or histogram-based methods for consistent contrast and downstream processing.

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Pupil

Overlay Images Using Weighted Overlay

Blend two images using weighted overlay for visualization, crossfade, or multi-exposure effects.

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Pupil

Pad Image

Add padding to an image on top, bottom, left, and right with configurable border handling and fill value.

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Pupil

Project Pixel to Camera Point

Convert a 2D pixel coordinate and depth value into a 3D point in camera coordinates using camera intrinsics and distortion coefficients.

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Pupil

Resize Image

Resize an image by scale factor or explicit width and height for flexible scaling in vision pipelines.

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Pupil

Resize Image With Aspect Fit

Resize an image to fit within target dimensions while preserving aspect ratio, with optional padding.

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Pupil

Rotate Image

Rotate an image by a specified angle with options to keep original dimensions or expand canvas.

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Pupil

Split Image Into Channels

Split a multi-channel image into individual color channels for per-channel processing and analysis.

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Pupil

Transform Using Pyramid Downsampling

Downsample an image using Gaussian pyramid smoothing and subsampling for multi-scale processing and efficient image analysis.

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Pupil

Transform Using Pyramid Upsampling

Upsample an image using Gaussian pyramid smoothing and interpolation for multi-scale reconstruction and image enlargement.

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Pupil

Filter Image Using Morphological Thinning Filter

Apply skeletonization (thinning) to binary images to reduce objects to their skeletal structure while preserving connectivity and topology.

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Pupil

Translate Image

Shift an image by a fixed number of pixels in horizontal and vertical directions with configurable border handling and interpolation.

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python examples/pupil_examples.py --example filter_image_using_sobel

Vitreous

List all available examples and run one:

python examples/vitreous_examples.py --list

python examples/vitreous_examples.py --example <name>

Vitreous

Add Point Clouds

Learn how to combine multiple 3D point clouds into a single unified cloud using the Telekinesis Agentic Skill Library. Perfect for multi-view fusion, sensor aggregation, and robotics pipelines.

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Vitreous

Apply Transform to Point Cloud

Learn how to move, rotate, or align 3D point clouds using the Telekinesis Agentic Skill Library. Ideal for Physical AI, robotics, computer vision, and multi-view sensor pipelines.

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Vitreous

Calculate Axis-Aligned Bounding Box

Learn how to compute the smallest 3D axis-aligned bounding box (AABB) for a point cloud using the Telekinesis Agentic Skill Library. Ideal for Physical AI, robotics, computer vision, and scene understanding pipelines.

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Vitreous

Calculate Oriented Bounding Box

Learn how to compute a tightly-fitted, orientation-aware 3D bounding box (OBB) for a point cloud using the Telekinesis Agentic Skill Library. Ideal for Physical AI, robotics, computer vision, pose estimation, and grasp planning.

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Vitreous

Calculate Point Cloud Centroid

Learn how to compute the geometric center (centroid) of a 3D point cloud using Telekinesis Vitreous. Useful for robotics, Physical AI, object localization, grasp planning, and reference frame computation.

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Vitreous

Cluster Point Cloud Based on Density Jump

Segment a point cloud into clusters by detecting sharp density changes along a chosen axis using Telekinesis Agentic Skill Library. Ideal for stacked objects, layered structures, or closely packed items in robotics and Physical AI pipelines.

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Vitreous

Cluster Point Cloud Using DBSCAN

Segment a 3D point cloud into clusters using DBSCAN from the Telekinesis Agentic Skill Library. Ideal for detecting distinct objects, handling noise, and separating spatially distributed items in robotics and perception pipelines.

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Vitreous

Convert Mesh to Point Cloud

Convert 3D triangle meshes into point clouds using the Telekinesis SDK. Ideal for 6D pose estimation, registration, and robotic perception pipelines with uniform point sampling and high fidelity geometry.

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Vitreous

Filter Point Cloud Using Bounding Box

Extract points within a 3D axis-aligned bounding box (AABB) to isolate regions of interest in point clouds for robotics, grasp planning, and object detection.

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Vitreous

Filter Point Cloud Using Cylinder Base Removal

Remove base points from cylindrical meshes to isolate object geometry for robotics applications like pose estimation, grasp planning, and cylinder alignment.

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Vitreous

Filter Point Cloud Using Mask

Apply a 2D boolean mask to selectively filter points from an organized point cloud for robotics tasks like object segmentation, ROI extraction, and grasp planning.

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Vitreous

Filter Point Cloud Using Oriented Bounding Box

Crop a point cloud to a rotated 3D region using an oriented bounding box (OBB) for robotics tasks like object isolation, pose estimation, and manipulation.

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Vitreous

Filter Point Cloud Using Plane Defined by Point Normal Proximity

Extract points near a plane defined by a reference point and normal vector for robotics tasks like planar surface extraction, ground removal, and tabletop detection.

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Vitreous

Filter Point Cloud Using Plane Proximity

Extract points near a plane defined by coefficients [a, b, c, d] for robotics tasks like planar surface extraction, ground removal, and tabletop detection.

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Vitreous

Filter Point Cloud Using Plane Splitting

Retain points on one side of a plane using coefficients [a, b, c, d]. Ideal for robotics tasks like ground removal, workspace segmentation, and object separation.

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Vitreous

Filter Point Cloud Using Radius Outlier Removal

Remove isolated or noisy points in a point cloud using radius-based neighbor filtering. Ideal for denoising, preprocessing, and improving 3D perception in robotics.

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Vitreous

Filter Point Cloud Using Statistical Outlier Removal

Remove noise from point clouds using statistical analysis of neighbor distances. Ideal for denoising, preprocessing, and improving 3D perception in robotics pipelines.

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Vitreous

Filter Point Cloud Using Uniform Downsampling

Reduce point cloud density by selecting every Nth point for faster processing. Ideal for robotics pipelines, preview generation, and preprocessing of large 3D datasets.

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Vitreous

Filter Point Cloud Using Voxel Downsampling

Reduce point cloud density using voxel downsampling. Ideal for preprocessing, registration, clustering, and segmentation in robotics and 3D point cloud workflows.

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Vitreous

Project Point Cloud to Plane

Learn how to project a 3D point cloud onto a plane using general plane coefficients [a, b, c, d] with the Vitreous SDK. Useful for robotics, AI, and physical pipelines in planar alignment, ground plane removal, and feature extraction.

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Vitreous

Project Point Cloud to Plane Defined by Point Normal

Learn how to project a 3D point cloud onto a plane using a reference point and normal vector with the Vitreous SDK. Useful for robotics, AI, and physical pipelines in planar alignment, registration, and feature extraction.

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Vitreous

Reconstruct Mesh Using Convex Hull

Learn how to reconstruct a convex mesh from a 3D point cloud using the Vitreous SDK. Ideal for robotics, AI, and physical pipelines in collision geometry, grasp planning, and simplified object modeling.

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Vitreous

Reconstruct Mesh Using Poisson

Learn how to reconstruct a smooth, watertight 3D mesh from a point cloud using Poisson surface reconstruction with the Vitreous SDK. Ideal for high-quality object surfaces in robotics, AI, and industrial pipelines.

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Vitreous

Register Point Clouds Using Cuboid Translation Sampler ICP

Learn how to align two point clouds with uncertain initial translation using Cuboid Translation Sampler ICP in the Vitreous SDK. Ideal for 6D pose estimation, object alignment, and industrial robotics.

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Vitreous

Register Point Clouds Using Fast Global Registration

Learn how to quickly align two point clouds using Fast Global Registration (FGR) with FPFH features in the Vitreous SDK. Ideal for initial 6D pose estimation, coarse alignment, and industrial robotics.

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Vitreous

Register Point Clouds Using Point-to-Plane ICP

Learn how to refine point cloud alignment using Point-to-Plane ICP in the Vitreous SDK. Ideal for high-accuracy registration, fine 6D pose estimation, and industrial robotics applications.

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Vitreous

Register Point Clouds Using Point-to-Point ICP

Learn how to perform fine-grained point cloud registration using Point-to-Point ICP in the Vitreous SDK. Ideal for precise 6D pose estimation and object alignment in robotics and industrial applications.

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Vitreous

Register Point Clouds Using Rotation Sampler ICP

Learn how to perform robust point cloud registration with unknown initial rotations using Rotation Sampler ICP in the Vitreous SDK. Ideal for 6D pose estimation and object alignment in robotics and industrial applications.

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Vitreous

Scale Point Cloud

Learn how to uniformly scale a point cloud about a center point using the Vitreous SDK. Ideal for resizing objects and scenes in robotics, industrial pipelines, and simulations.

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Vitreous

Segment Point Cloud Using Color

Learn how to segment point clouds by color using the Vitreous SDK. Ideal for identifying and isolating objects based on color cues in robotics, industrial pipelines, and 3D perception.

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Vitreous

Segment Point Cloud Using Plane

Learn how to segment planar surfaces from point clouds using the Vitreous SDK. Ideal for detecting floors, walls, tables, and other flat surfaces in robotics, industrial pipelines, and 3D perception.

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Vitreous

Subtract Point Clouds

Learn how to subtract one point cloud from another using the Vitreous SDK. Ideal for isolating objects, removing backgrounds, or filtering known surfaces in robotics, industrial pipelines, and 3D perception.

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python examples/vitreous_examples.py --example scale_point_cloud

Datatypes

List all available examples and run one:

python examples/datatypes_examples.py --list

python examples/datatypes_examples.py --example <name>

datatypes

Bool

Single boolean value.

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datatypes

Int

32-bit signed integer.

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datatypes

Float

32-bit floating-point number.

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datatypes

String

UTF-8 encoded text string.

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datatypes

Rgba32

Packed RGBA color representation.

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datatypes

Image

Image data as NumPy array or raw bytes.

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datatypes

ImageFormat

Image format metadata and encoding information.

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datatypes

Vector2D

2D vector with x and y components.

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datatypes

Vector3D

3D vector with x, y, and z components.

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datatypes

Vector4D

4D vector with x, y, z, and w components.

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datatypes

Mat3X3

3×3 matrix for 2D transformations.

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datatypes

Mat4X4

4×4 matrix for 3D transformations and poses.

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datatypes

Position2D

2D position with x and y coordinates.

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datatypes

Position3D

3D position with x, y, and z coordinates.

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datatypes

Points2D

2D point cloud or array of 2D points.

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datatypes

Points3D

3D point cloud or array of 3D points.

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datatypes

ListOfPoints3D

List containing multiple 3D point clouds.

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datatypes

Boxes2D

2D bounding boxes with multiple format support.

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datatypes

Boxes3D

3D bounding boxes with poses and rotations.

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datatypes

LineStrips2D

2D polylines and contours.

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datatypes

Circles

2D circles defined by centers and radii.

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datatypes

Ellipses

2D ellipses with centers, axes, and angles.

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datatypes

Lines

2D lines represented in polar form (rho, theta).

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datatypes

Mesh3D

3D triangle mesh with vertices and faces.

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datatypes

Categories

Category definitions for annotations.

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datatypes

ObjectDetectionAnnotations

Object detection and instance segmentation annotations.

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datatypes

PanopticSegmentationAnnotation

Panoptic segmentation with masks and segment information.

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datatypes

GeometryDetectionAnnotations

Geometric shape detection annotations for circles, ellipses, and lines.

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datatypes

Part

Part with ID, 6-DOF pose, dimensions, and state.

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datatypes

Tray

Tray container with ID, pose, dimensions, and slot count.

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datatypes

Bin

Bin container with ID, pose, and parts.

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python examples/datatypes_examples.py --example bool

Medulla

Each script is run directly with:

python examples/medulla/<vendor>/<script>.py

medulla

Connect

Establishes a connection to a hardware device.

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medulla

Disconnect

Closes the connection to a hardware device.

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medulla

Capture Color Image

Captures a single color image from a camera.

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medulla

Capture Depth Image

Captures a single depth image from a depth camera.

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medulla

Capture Point Cloud

Captures a 3D point cloud from a depth camera.

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medulla

Stream Live Color Video

Streams live color video from a camera.

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medulla

Stream Live Depth Video

Streams live depth video from a depth camera.

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medulla

Stream Point Cloud

Streams live 3D point clouds from a depth camera.

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medulla

Get Parameter

Retrieves a parameter value from a camera.

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medulla

Set Parameter

Sets a parameter value on a camera.

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medulla

Load Settings

Loads camera settings from a file or preset.

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medulla

Save Settings

Saves current camera settings to a file.

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medulla

Get Intrinsics

Retrieves camera intrinsic calibration parameters.

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medulla

Publish Video with BabyROS

Publishes camera video stream over BabyROS.

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python examples/medulla/webcam/quickstart.py

Synapse

Each script is run directly with:

python examples/synapse/<script_or_subdir/script>.py

Connection and Motion

synapse

Connection and Disconnection

Establishes and closes robot connections.

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synapse

Set Cartesian Pose

Moves the end-effector to a target Cartesian pose.

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synapse

Set Joint Positions

Commands the robot to specific joint positions.

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synapse

Set Cartesian Pose in Joint Space

Reaches a Cartesian target via joint-space planning.

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synapse

Set Joint Position in Cartesian Space

Commands joint positions while tracking a Cartesian-space path.

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synapse

Move until Contact

Moves the robot along a direction until contact is detected.

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synapse

Stop Cartesian Motion

Stops an ongoing Cartesian motion command.

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synapse

Stop Joint Motion

Stops an ongoing joint motion command.

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synapse

Trigger Protective Stop

Triggers a protective stop to safely halt the robot.

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synapse

Jog Mode

Enables and disables jog mode for incremental manual movement.

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synapse

Freedrive Mode

Enables and disables freedrive (gravity-compensated hand-guiding) mode.

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synapse

Teach Mode

Enables and disables teach mode for recording robot poses.

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synapse

Contact Detection

Detects contact events during robot motion.

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Kinematics

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Forward Kinematics

Computes end-effector pose from joint positions.

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synapse

Inverse Kinematics

Solves joint positions for a target Cartesian end-effector pose.

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synapse

Setup Kinematics Solver

Initializes and configures the kinematics solver for a robot model.

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synapse

Get Link Transforms

Retrieves the transformation matrices for each robot link.

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synapse

Set Default Joint Configuration

Sets the default joint configuration used as IK seed.

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Servo Control

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Servo Joint

Streams joint position targets at high frequency for servo control.

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synapse

Servo Cartesian

Streams Cartesian pose targets at high frequency for servo control.

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Servo Circular

Executes a circular trajectory using servo control.

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synapse

Servo Stop

Stops an active servo motion.

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Force Control

synapse

Is Tool in Contact

Detects whether the robot tool is currently in contact with an object.

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State Reading

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Manipulator States

Reads comprehensive manipulator state information.

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Is Connected

Checks whether the SDK is connected to the robot.

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synapse

Get Joint Positions

Reads the current joint positions.

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synapse

Get Joint Velocities

Reads the current joint velocities.

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synapse

Get Joint Torques

Reads the current joint torques.

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synapse

Get Cartesian Pose

Reads the current end-effector Cartesian pose.

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synapse

Get TCP Speed

Reads the actual TCP (tool center point) speed.

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synapse

Get TCP Force

Reads the actual force/torque at the TCP.

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synapse

Get Target Joint Positions

Reads the target joint positions commanded to the controller.

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synapse

Get Target Joint Velocities

Reads the target joint velocities commanded to the controller.

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synapse

Get Target Joint Accelerations

Reads the target joint accelerations commanded to the controller.

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synapse

Get Target TCP Pose

Reads the target TCP pose commanded to the controller.

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synapse

Get Target TCP Speed

Reads the target TCP speed commanded to the controller.

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Get Timestamp

Reads the robot controller timestamp.

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Robot Status

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Get Robot Mode

Reads the current robot operating mode.

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Get Robot Status

Reads the overall robot status.

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Get Safety Mode

Reads the current safety mode of the robot.

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Get Runtime State

Reads the runtime execution state of the robot program.

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synapse

Get Controller Frequency

Reads the control loop frequency of the robot controller.

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Diagnostics

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Get Speed Scaling Combined

Reads the combined speed scaling factor applied by the controller.

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synapse

Get Target Speed Fraction

Reads the target speed fraction set for the robot program.

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Tools

synapse

Tool Connection and Disconnection

Connects to and disconnects from a robot tool (e.g. gripper).

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synapse

Tool Set Unit

Sets the measurement units used by a robot tool.

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synapse

Tool Set Position Range

Configures the position range limits of a robot tool.

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synapse

Tool Set Speed

Sets the movement speed of a robot tool.

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synapse

Tool Set Force

Sets the gripping force of a robot tool.

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synapse

Tool Open

Opens a robot tool (e.g. gripper).

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synapse

Tool Close

Closes a robot tool (e.g. gripper).

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synapse

Tool Move

Moves a robot tool to a specified position.

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synapse

Tool Get Current Position

Reads the current position of a robot tool.

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python examples/synapse/quickstart_set_cartesian_pose_universal_robots.py

Where to Go Next

Applications

Real-world use cases: pick-and-place, palletizing, quality inspection, and more.

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Explore the Docs

Skills

Vision, 3D, hardware, and robotics skills — the full API reference.

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Agents

Turn natural-language instructions into robot code with Tzara, the VS Code agent.

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Data Engine

Generate and manage synthetic datasets for training Physical AI models.

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BabyROS

Lightweight pub/sub and service primitives for building robot communication layers.

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Applications

Real-world use cases: pick-and-place, palletizing, quality inspection, and more.

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Support

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