KinetiqVision

Training & tutorials

Computer vision and robotics learning paths

Structured paths from imaging and geometry through deep vision, frames and kinematics, and perception — similar to how cloud training catalogs group courses by role and topic. Open any row to study in order, or jump to a path below.

Learning paths

Each path is a sequence of lessons. Your progress is saved in this browser when you open a lesson.

Learning path·Beginner·~12h

Computer Vision Foundations

A structured path from light and pixels to geometry, classical features, and modern deep networks — with checkpoints and exercises at every step.

What you'll learn

  • Imaging
  • Geometry
  • Features
  • Deep learning
  • Deployment
Curriculum & progressStart first lesson

Module 1. Imaging & digital images

How cameras turn light into arrays of numbers, and how we preprocess those arrays for algorithms.

  1. Lesson55 min

    Light, sensors, and the imaging pipeline

    Radiance, photons, sensor noise, demosaicing, gamma, and the end-to-end path from scene to stored image.

  2. Lesson60 min

    Pixels, convolution, and edges

    Grids, channels, linear filters, separable kernels, gradients, and building intuition before neural networks.

Module 2. Geometry & correspondence

Pinhole cameras, projection, epipolar geometry, and how 2D images relate to 3D structure.

  1. Lesson65 min

    Camera models and projection

    Intrinsic and extrinsic parameters, the pinhole model, lens distortion, and projecting 3D points into pixels.

  2. Lesson70 min

    Features, matching, and robust estimation

    Corners, descriptors, putative matches, RANSAC, homographies, and when planar models break.

Module 3. Learning-based vision

Convolutional networks for classification, detection, and segmentation — plus practical constraints on mobile and edge.

  1. Lesson75 min

    Convolutional networks for images

    From fully-connected layers to conv blocks, receptive fields, pooling, and training objectives.

  2. Lesson70 min

    Detection, segmentation, and on-device trade-offs

    Anchors vs queries, instance vs semantic segmentation, latency, memory, and quantization at a high level.

Learning path·Beginner·~11h

Robotics Foundations

Coordinate frames, rigid motion, kinematics, and how robots use sensors — structured as a progression with exercises.

What you'll learn

  • Frames
  • Kinematics
  • IK
  • Sensors
  • Vision–action
Curriculum & progressStart first lesson

Module 1. Frames & rigid motion

Representing pose, composing transforms, and moving between world, base, and end-effector coordinates.

  1. Lesson50 min

    Coordinate frames and transforms

    Right-handed frames, translation vectors, rotation matrices, and the rules for chaining transforms.

  2. Lesson60 min

    Rigid body motion in 2D and 3D

    Special orthogonal groups, Euler vs axis–angle, and homogeneous coordinates for compact composition.

Module 2. Kinematics

Forward and inverse kinematics for serial arms: from joint angles to Cartesian pose and back.

  1. Lesson65 min

    Forward kinematics and the product of exponentials

    DH parameters vs PoE, building the chain, and computing the tool pose from joint configuration.

  2. Lesson70 min

    Inverse kinematics: redundancy and numerical methods

    Analytic vs numerical IK, Jacobians, null-space motion, and practical pitfalls near singularities.

Module 3. Perception & closed-loop control

How robots sense the world and close the loop from perception to motion — including vision basics for manipulation.

  1. Lesson55 min

    Sensors, state, and uncertainty

    Proprioception, IMUs, lidar, cameras, noise models, and why state estimation treats belief distributions.

  2. Lesson60 min

    From images to motions (intro)

    Hand–eye calibration, visual servoing at a high level, and how CV lessons connect to robot motion.

More resources

Shorter articles and product-focused notes live on the blog.

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