robolab-taskgen

Generate Python robot manipulation task definitions for the RoboLab benchmark framework.

423|59|Updated Apr 8, 2026
One-click install
npx skills add https://github.com/NVlabs/RoboLab --skill robolab-taskgen
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: robolab-taskgen
Source: https://github.com/NVlabs/RoboLab/tree/main/skills/robolab-taskgen
Command: npx skills add https://github.com/NVlabs/RoboLab --skill robolab-taskgen

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill removes the complexity of manually authoring robot manipulation tasks by automating the creation of Python task definitions, success conditions, and instruction variants from natural language.

Core Features & Use Cases

  • Automated Task Generation: Converts natural language goals into structured Python task classes compatible with NVIDIA Isaac Lab.
  • Intelligent Condition Mapping: Automatically maps user goals to predefined termination functions like object_in_container or stacked.
  • Use Case: Quickly generate a new pick-and-place benchmark task by describing the scene and the desired robot action, ensuring all required termination and subtask logic is correctly formatted.

Quick Start

Use the robolab-taskgen skill to create a new task file for picking up a red block and placing it on a blue block in the workspace.

Frequently Asked Questions about robolab-taskgen

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate robot manipulation tasks for Isaac Lab using natural language?

To generate robot manipulation tasks for Isaac Lab, provide a natural language description of your scene and goals. The skill automatically outputs structured Python task classes, subtask decomposition, and mapped termination criteria like object_in_container.

What do I need to create a robot manipulation benchmark task from a USD scene file?

Creating a manipulation benchmark task requires valid USD scene assets and access to the robolab.core.task module. These inputs ensure environment compatibility when the skill generates the Python task definitions and success conditions.

Can I automatically map natural language goals to predefined termination functions in robot simulations?

You can map natural language goals to predefined termination functions in robot simulations automatically. The skill interprets your text input and links it to specific termination criteria like stacked or object_in_container within the generated Python code.

Does robolab-taskgen support subtask decomposition for complex pick-and-place benchmark tasks?

Robolab-taskgen supports subtask decomposition for complex pick-and-place benchmark tasks. It breaks down the natural language input into structured subtasks and formats all required termination logic within the generated Python task class.

Why manually authoring robot manipulation task definitions is difficult and how to automate it?

Manually authoring robot manipulation task definitions is complex due to formatting termination criteria and subtask logic. You can automate this by using natural language input to generate structured Python task classes compatible with the RoboLab benchmark framework.