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pipergo2-demo

Orchestrate deterministic PiperGo2 demo routing for navigation and pick tasks in PyBullet simulation.

Updated Apr 27, 2026
Search Tags:#navigation#simulation#demo#robotics#vla#pipergo2
One-click install
npx skills add https://github.com/DevMewada1299/ClearBot --skill pipergo2-demo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pipergo2-demo
Source: https://github.com/DevMewada1299/ClearBot/tree/main/PhyAgentOS/skills/pipergo2-demo
Command: npx skills add https://github.com/DevMewada1299/ClearBot --skill pipergo2-demo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a deterministic demonstration routing pipeline for PiperGo2 in a simulated environment, coordinating open simulation, desk navigation, and staged picks to showcase rule-based and learned-control behaviors.

Core Features & Use Cases

  • Deterministic routing: Orchestrates a fixed sequence of actions to demonstrate PiperGo2 capabilities in a safe, simulated setting.
  • Dual-mode pick strategies: Supports both rule-based pickup and SmolVLA-based pickup without requiring a full return to home between steps.
  • Demo-oriented scenarios: Useful for QA, training, and explorative testing of navigation, manipulation, and policy execution in a contained workspace.

Quick Start

Execute the demo sequence to open the simulation, navigate to the desk, pick and return a red cube using the rule-based path, then deploy a VLA to pick the red cube.

Frequently Asked Questions about pipergo2-demo

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

FAQPage Schema
How do I run a deterministic navigation and pick demo for a robot in PyBullet simulation?▼

You can run a deterministic navigation and pick demo in PyBullet simulation by executing a fixed routing sequence that opens the environment, navigates to a desk, picks a red cube, and returns it.

What is the difference between rule-based and VLA-based pick strategies in robot simulation?▼

Rule-based picking executes scripted actions for object retrieval, while VLA-based picking deploys a learned policy. This demo supports both strategies sequentially without requiring a full return to home between steps.

Can I use a VLA model for robotic manipulation without returning the arm to home position?▼

Yes, this demo pipeline supports executing a VLA-based pick immediately after a rule-based pick, allowing staged manipulation behaviors without forcing a full return to home between operational steps.

How do I automate QA testing for robotic navigation and manipulation in a simulated environment?▼

You can automate QA testing for robotic navigation and manipulation by orchestrating a deterministic demo routing sequence in PyBullet, coordinating scripted actions and closed-loop policy execution for explorative testing.

Does this robot simulation demo support staged pick and navigation tasks for training purposes?▼

Yes, the demo is designed for QA, training, and demonstration tasks, orchestrating named navigation and closed-loop pick execution in a contained PyBullet workspace with open-source drivers.

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