orcahand

Control the 17-DOF ORCA Hand across hardware and MuJoCo simulation backends.

3|2|Updated Mar 17, 2026
One-click install
npx skills add https://github.com/broomva/skills --skill orcahand
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: orcahand
Source: https://github.com/broomva/skills/tree/main/skills/robotics/orcahand
Command: npx skills add https://github.com/broomva/skills --skill orcahand

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mujoco, gymnasium, numpy, dynamixel-sdk, torch, pytorch_kinematics, pyyaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill provides a comprehensive interface for managing the ORCA Hand, a 17-DOF tendon-driven robotic hand, bridging the gap between physical hardware control, MuJoCo simulation, and reinforcement learning training.

Core Features & Use Cases

  • Unified Plant Interface: Seamlessly switch between physical hardware (orca_core) and simulated environments (orca_sim) using identical typed schemas.
  • Safety & EGRI: Implements robust safety shields (ROM, current, temperature) and an Evaluator-Governed Recursive Improvement loop for policy optimization.
  • Use Case: Use this skill to build and calibrate a physical ORCA Hand, train grasp policies in MuJoCo, or teleoperate the hand using Apple Vision Pro or Rokoko gloves.

Quick Start

Use the orcahand skill to bootstrap your workspace and detect the connected hardware.

Frequently Asked Questions about orcahand

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

FAQPage Schema
How do I control a 17-DOF tendon-driven robotic hand in MuJoCo simulation?

This skill provides a full-stack control interface for the 17-DOF ORCA Hand, bridging physical hardware control, MuJoCo simulation, and reinforcement learning training using identical typed schemas for both backends.

What's the best way to train grasp policies for a dexterous robotic hand?

To train grasp policies for a dexterous robotic hand, use the Evaluator-Governed Recursive Improvement loop within MuJoCo simulation, which maintains safety shields for range of motion, current, and temperature during policy optimization.

Can I teleoperate a robotic hand using Apple Vision Pro or Rokoko gloves?

Yes, you can teleoperate a robotic hand using Apple Vision Pro or Rokoko gloves by integrating with orca_core, orca_sim, and orca_retargeter to maintain safety shields and multi-rate control loops.

Do I need Dynamixel SDK and PyTorch to set up robotic hand hardware?

Yes, setting up robotic hand hardware requires the Dynamixel SDK, PyTorch, mujoco, gymnasium, numpy, pytorch_kinematics, and pyyaml to assemble, calibrate, and control the physical hardware and train RL policies.

How does a unified plant interface handle safety for robotic hand reinforcement learning?

A unified plant interface handles safety for robotic hand reinforcement learning by implementing robust safety shields for range of motion, current, and temperature, combined with an Evaluator-Governed Recursive Improvement loop for safe policy optimization.

What are the limitations of using MuJoCo for robotic hand teleoperation?

Limitations of using MuJoCo for robotic hand teleoperation include the strict requirement to integrate orca_core, orca_sim, and orca_retargeter to maintain safety shields and multi-rate control loops when switching to physical hardware.