Grasping Short Axis logo

Grasping Short Axis

Organization
graph-robots
grasping-short-axis

Deterministic short-axis-aligned grasp with CuRobo. The grasp pose is computed geometrically (NOT sampled/scored) — the gripper approaches straight down with its finger-opening axis locked to the OBB's SHORTER horizontal axis, so the jaws close ACROSS the narrow dimension of an elongated target. Use for pan/pot handles, bottles, tools, utensils — anywhere the OBB centroid is graspable but orientation is the thing that matters. An optional offset_from_base node slides the grasp outward along the handle, clear of the heavier object body, when a second OBB of that body is wired in. A scored/sampled pose often holds at close but shears out under the lift; the short-axis pose grips the bar squarely so it survives lift + transport.

Overview

Publishergraph-robots
Repositoryopen-robot-skills
Skill namegrasping-short-axis
Stars
58
Forks
8
Bundled files
9
LicenseApache-2.0
Links
  • 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.

  • 9 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 Grasping Short Axis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/graph-robots/open-robot-skills/tree/main/skills/grasping-short-axis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/grasping-short-axis .claude/skills/grasping-short-axis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Grasping Short Axis 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 Grasping Short Axis 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 Grasping Short Axis 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.

grasping-short-axis

Deterministic, geometry-locked grasping with CuRobo. The grasp pose is computed directly from the target OBB — the gripper descends along world −Z with its finger-opening axis snapped to the OBB's shorter horizontal axis, so the jaws close across the narrow dimension of the bar. An optional node then slides the grasp outward along the handle, clear of a heavier attached body. The subgraph builds a per-observation collision world (target excluded), plans a CuRobo trajectory, executes it, finalizes the last waypoint, closes the gripper, and outputs the EE pose at grasp.

Install

This skill depends on the curobo and geometry tool bundles:

bash
export CUDA_HOME=/usr/local/cuda
uv sync --extra curobo --extra geometry   # (pip: pip install -e "open-robot-skills[curobo,geometry]" --no-build-isolation)

When to use

  • Elongated targets where grasp orientation matters: pan / pot handles, bottles, tools, utensils.
  • A subpart (handle) that protrudes from a heavier body — wire base_obb for the outward slide.
  • When a sampled/scored grasp pose holds at close but the object slips during the lift (marginal off-axis contact patch).

When NOT to use

  • Symmetric objects (boxes, cans) where any yaw works — use grasping-with-planner.
  • curobo not deployed.
  • You genuinely want multiple sampled candidates for the planner to choose from — use grasping-with-planner.

Recommended subgraph state flow

10 states, in order:

text
open → compute_grasp → offset_from_base → approach → observe
     → build_world → plan → execute → finalize → close → grasped

(grasped is the success-marker noop from sg.add_exit("grasped"), with an edge to END.)

State details:

  1. open — type: tool, tool: "robot.open_gripper", inputs: { settle_steps: 40 }.
  2. compute_grasp — type: script, file scripts/<sg>/short_axis_grasp_pose.py (canonical — do NOT re-emit a ```python block; the bundle materializes it). Inputs: target_obb = Ref("in.target_obb"). Optional z_offset (default −0.04: descend the fingertip 4 cm into the OBB top). Returns {grasp_pose: Se3Pose}.
  3. offset_from_base — type: script, file scripts/<sg>/offset_grasp_from_base.py (canonical). Inputs: handle_obb = Ref("in.target_obb"), grasp_pose = Ref("compute_grasp.grasp_pose"), and — ONLY when a body perception was authored and base_obb declared as a subgraph input — base_obb = Ref("in.base_obb"). Returns {adjusted_grasp: Se3Pose}. Safe no-op when base_obb is absent.
  4. approach — type: script, file scripts/<sg>/approach_above.py (canonical). Inputs: target_position = Ref("offset_from_base.adjusted_grasp.position"), rotation = Ref("offset_from_base.adjusted_grasp.rotation"), target_obb = Ref("in.target_obb").
  5. observe — type: tool, tool: "robot.get_observation".
  6. build_world — type: script, file scripts/<sg>/build_world.py (canonical). Inputs: observation = Ref("observe"), target_mask = Ref("in.target_mask"), target_obb = Ref("in.target_obb"), target_name = "target".
  7. plan — type: script, file scripts/<sg>/plan_grasp.py (canonical). Inputs: world_config = Ref("build_world.config"), observation = Ref("observe"), grasp_poses = Ref("offset_from_base.adjusted_grasp"), target_name = "target". plan_grasp.py auto-wraps the single bare Se3Pose into a one-element list. All four inputs are required.
  8. execute — type: tool, tool: "robot.execute_trajectory", inputs: { trajectory: Ref("plan.trajectory"), subsample: 4 }.
  9. finalize — type: script, file scripts/<sg>/finalize_trajectory.py (canonical). Inputs: trajectory = Ref("plan.trajectory"). MANDATORY — see the execute → finalize → close hard_rule. Edges: execute → finalize, finalize → close.
  10. close — type: tool, tool: "robot.close_gripper", inputs: { settle_steps: 60 }. Edge directly from close to the grasped success marker.

The cross-subgraph output binding:

python
sg.set_outputs(
    ee_pose_at_grasp=Ref("observe.arms.0.ee_pose"),
    grasp_pose=Ref("offset_from_base.adjusted_grasp"),
)

Wire the exit:

python
sg.add_edge("close", "grasped")
sg.add_edge("grasped", END)

sg.add_exit("grasped")
sg.set_on_error("failed")

Required end states

End stateMeaning
graspedGripper has closed on the object after the descend. Route to the next subgraph (typically transporting-objects).
failedAny grasp-attempt failure: collision-aware planning failure or trajectory execution error (a raise to on_error). Lives only in on_error — never declare a failed node.

Checkpoints

Express grasp success as a validate=True postcondition checkpoint target_held on the close state (the gripper is holding the target after close). Do not add a re-check-and-raise node.

See also

  • ../grasping-with-planner/SKILL.md — the sampled-candidate OBB counterpart; this skill mirrors its approach/build_world/plan/finalize tail (the scripts are bundled here so the skill is self-contained).

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 Grasping Short Axis AI skill do?

Deterministic short-axis-aligned grasp with CuRobo. The grasp pose is computed geometrically (NOT sampled/scored) — the gripper approaches straight down with its finger-opening axis locked to the OBB's SHORTER horizontal axis, so the jaws close ACROSS the narrow dimension of an elongated target. Use for pan/pot handles, bottles, tools, utensils — anywhere the OBB centroid is graspable but orientation is the thing that matters. An optional offset_from_base node slides the grasp outward along the handle, clear of the heavier object body, when a second OBB of that body is wired in. A scored/sa...

Why use Grasping Short Axis on TypingMind?

Because you install it once and use it with any model. Grasping Short Axis 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 Grasping Short Axis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/graph-robots/open-robot-skills/tree/main/skills/grasping-short-axis. 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 Grasping Short Axis?

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 Grasping Short Axis?

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

Is the Grasping Short Axis 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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