Reconstructing Collision Worlds

Organization

graph-robots

reconstructing-collision-worlds

Reconstructs a planner collision world from calibrated RGB-D views alone, excluding the grasp target, the robot, and a perceived fixture mask, dropping depth-edge slivers and invalid-depth sheets, and carving the free space an intentional-contact goal needs as an approach tube along a fixture axis or a corridor through a lid aperture. Use when a held-object motion must be planned against obstacles no simulator or CAD scene supplies, before registering the object and planning the carry.

Overview

Publisher	graph-robots
Repository	`open-robot-skills`
Skill name	`reconstructing-collision-worlds`
Stars	58
Forks	8
Bundled files	2
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by graph-robots on GitHub. Read the source before you install it.

Installation

Install the Reconstructing Collision Worlds AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/graph-robots/open-robot-skills/tree/main/skills/reconstructing-collision-worlds
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/graph-robots/open-robot-skills/tree/main/skills/reconstructing-collision-worlds

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/graph-robots/open-robot-skills.git /tmp/open-robot-skills
mkdir -p .claude/skills
cp -r /tmp/open-robot-skills/skills/reconstructing-collision-worlds .claude/skills/reconstructing-collision-worlds

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reconstructing Collision Worlds in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Reconstructing Collision Worlds on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Reconstructing Collision Worlds is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

reconstructing-collision-worlds

Run once after perception and before grasping, with the same observation the perception skills used. build_collision_world merges depth from the selected views (camera_names, comma-separated; empty = all) into alpha-shape meshes through geometry.build_world_config and returns a WorldConfig plus the mesh names.

What is excluded

The grasp target by its perceived target_mask in mask_camera_name (default overhead), and by segmenting target_description in every other selected view so the object does not become a static obstacle where it later rotates in the hand.
The robot, by text segmentation per view and by motion.get_robot_collision_spheres when the connector provides it (a connector without it falls back to the visual masks).
A perceived fixture_mask from the mask camera, unless it is a full-frame placeholder that would erase the world.

What is carved

An approach tube of 55 mm radius along outward_sign * fixture_axis ending 12 mm behind fixture_tip, when both are given. Use outward_sign: -1 for an axis that points into the fixture (an aperture's inward axis) and 1 for one that points toward its free side (a shaft).
A vertical corridor of corridor_radius through corridor_center between corridor_rim_z - 35 mm and +180 mm, when the centre and a positive radius are given. The radius is the caller's measurement (for example a fraction of the perceived aperture radius); the script adds no margin.

Cleaning and packing

Sparse, nearly zero-thickness sheets under 15 cm are dropped; components lying entirely below sheet_floor_z or spanning more than sheet_max_extent_m are invalid-depth returns and are dropped too (0 disables either gate). voxel_size trades surface ripple for detail. pack_meshes: true folds every component into one perceived_scene mesh for planners whose scene cache admits few named obstacles.

Boundaries

No simulator state, CAD model, or fixed world coordinate is read.
The script raises when no mesh survives; the graph routes that to failed.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Reconstructing Collision Worlds AI skill do?

Why use Reconstructing Collision Worlds on TypingMind?

Because you install it once and use it with any model. Reconstructing Collision Worlds is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Reconstructing Collision Worlds in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/graph-robots/open-robot-skills/tree/main/skills/reconstructing-collision-worlds. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Reconstructing Collision Worlds?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Reconstructing Collision Worlds?

As many as you like. As long as a model supports skills, you can use Reconstructing Collision Worlds with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Reconstructing Collision Worlds AI skill free?

Yes. It is published on GitHub by graph-robots under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

reconstructing-collision-worlds

What is excluded

What is carved

Cleaning and packing

Boundaries

Bundled files

Frequently asked questions

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