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Robot Perception

arpitg1304/robotics-agent-skills

This is a solid reference for anyone building vision systems on robots. It covers the practical stuff you actually need: camera calibration workflows with OpenCV, sensor comparisons (RealSense, ZED, OAK-D), and how to handle multi-sensor fusion. The code samples focus on intrinsic calibration with real warnings about coverage and reprojection error, which is helpful since bad calibration kills everything downstream. You'll want this if you're setting up perception pipelines, debugging why your depth maps look wrong, or figuring out which sensor to buy for manipulation versus navigation. It assumes you know Python and basic computer vision but walks through the robotics-specific parts like coordinate transforms and sensor sync.

skills.sh installs
273
GitHub stars
400
last updated
May 25, 2026
View on GitHub

Install

$ npx -y skills add arpitg1304/robotics-agent-skills --skill robot-perception --agent claude-code

Installs into .claude/skills of the current project.

Files

Robot Perception Skill

When to Use This Skill

  • Setting up and configuring camera, LiDAR, or depth sensors
  • Building RGB, depth, or point cloud processing pipelines
  • Calibrating cameras (intrinsic, extrinsic, hand-eye)
  • Implementing object detection, segmentation, or tracking for robots
  • Fusing data from multiple sensor modalities
  • Streaming sensor data with proper threading and buffering
  • Synchronizing multi-sensor rigs
  • Deploying perception models on robot hardware (GPU, edge)
  • Debugging perception failures (latency, dropped frames, misalignment)

Sensor Landscape

Sensor Types and Characteristics

SKILL.md on GitHub

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Details

Repository
github.com/arpitg1304/robotics-agent-skills
Category
AI & Agent Building
License
Apache-2.0
First seen
Jun 11, 2026

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