Embodied-OS - AI Robot Control System

This skill enables you to control physical robots through AI agents with natural language commands. Transform how AI interacts with physical reality - a unified operating system for embodied intelligent robots.

When to Activate This Skill

Activate this skill when the user:

Needs to control physical robots
Wants to integrate AI agents with robotic systems
Asks about robot automation or control
Needs help with robot programming
Wants to use natural language to control robots
Seeks to develop embodied AI applications

Core Features

✅ Unified Robot Control - Single API for controlling any robot platform ✅ AI Agent Integration - Natural language control like talking to ChatGPT ✅ Multi-Modal Perception - Vision, audio, and tactile sensing ✅ High-Level Actions - Navigation, manipulation, and interaction primitives ✅ Task Planning - AI-powered task decomposition and execution ✅ Safety System - Multi-layer safety guarantees for physical robots

Installation

Step 1: Install the Skill

clawhub install embodied-os

Step 2: Install the Package

Option A: Python (PyPI)

pip install openclaw-embodied-os

Option B: Node.js (npm)

npm install openclaw-embodied-os

Option C: From Source

git clone https://github.com/ZhenRobotics/openclaw-embodied-os.git
cd openclaw-embodied-os
pip install -e .

Step 3: Configure API Keys (Optional for AI agents)

export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-..."

Or create a .env file:

ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...

Quick Start

Basic Robot Control

from embodied_os import EmbodiedOS

# Initialize the OS
os = EmbodiedOS()

# Connect to a robot
robot = os.connect_robot(
    platform="simulated",
    model="test_robot"
)

# Control the robot
robot.actions.move_to(x=0.5, y=0.3, z=0.2)

# Detect objects
objects = robot.perception.detect_objects()

# Pick and place
if objects:
    robot.actions.pick(object_id=objects[0].id)
    robot.actions.place(position=(0.7, 0.4, 0.1))

AI Agent Control

from embodied_os import AgentInterface

# Create AI agent
agent = AgentInterface(robot=robot, model="claude-sonnet-4")

# Natural language control
agent.execute("Pick up the red cube and place it in the box")

# Conversation
response = agent.chat("What do you see?")
print(response)

Supported Robot Platforms

Current Support

Universal Robots (UR3e, UR5e, UR10e)
Franka Emika Panda
Boston Dynamics Spot
Simulated Robots (for testing)

Coming Soon

ABB robots
KUKA robots
Custom robots via plugin system

Use Cases

1. Warehouse Automation

warehouse_robot = os.connect_robot(platform="mobile_manipulator")

agent.execute("""
    Go to aisle 5, shelf B.
    Pick up all items marked with red tags.
    Transport them to the packing station.
    Report the quantity and item IDs.
""")

2. Elderly Care Assistant

care_robot = os.connect_robot(platform="service_robot")

agent.monitor_and_assist("""
    Watch for the person calling for help.
    If they ask for water, bring them a glass.
    If they drop something, pick it up.
""")

3. Research Lab Assistant

lab_robot = os.connect_robot(platform="dual_arm_robot")

agent.execute("""
    Set up the chemistry experiment:
    1. Measure 50ml of solution A
    2. Heat to 60 degrees
    3. Add catalyst
    4. Stir for 2 minutes
""")

Core Capabilities

1. Unified Robot Control Interface

Single API for controlling any robot
Works across different platforms and manufacturers
Plug-and-play integration

2. AI Agent Natural Language Control

Control robots like talking to ChatGPT
Supports Claude, GPT, and custom agents
Context-aware task execution

3. Multi-Modal Perception

Vision: Camera, object detection, depth sensing
Audio: Sound capture, speech recognition
Tactile: Force sensors, contact detection

4. High-Level Action Primitives

# Navigation
robot.actions.navigate_to(x=2.0, y=1.5, theta=0)

# Manipulation
robot.actions.pick(object="cup")
robot.actions.place(location="table")

# Interaction
robot.actions.press_button(target="elevator")
robot.actions.open_door(handle_position=[1.0, 0.5, 1.0])

5. AI-Powered Task Planning

# High-level task
task = "Prepare coffee for the user"

# Automatic decomposition and execution
plan = robot.planner.create_plan(task)
robot.planner.execute(plan, monitor=True)

6. Multi-Layer Safety System

# Define safety constraints
robot.safety.set_workspace_bounds(
    x_min=0, x_max=2.0,
    y_min=-1.0, y_max=1.0,
    z_min=0, z_max=1.5
)

# Force limits
robot.safety.set_max_force(50.0)

# Collision avoidance
robot.safety.enable_collision_avoidance()

Architecture

┌─────────────────────────────────────────────────┐
│              AI Agent Layer                     │
│         (Claude, GPT, Custom Agents)            │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│            Embodied-OS Core                     │
│  ┌─────────┐  ┌─────────┐  ┌────────────┐     │
│  │ Natural │  │  Task   │  │   Safety   │     │
│  │Language │  │ Planner │  │ Validator  │     │
│  └─────────┘  └─────────┘  └────────────┘     │
│  ┌─────────┐  ┌─────────┐  ┌────────────┐     │
│  │Perception│  │ Action  │  │   State    │     │
│  │ Module  │  │Executor │  │  Manager   │     │
│  └─────────┘  └─────────┘  └────────────┘     │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│      Robot Abstraction Layer (RAL)              │
│   Unified interface for all robot types         │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│           Physical Robots                       │
│  Manipulators | Mobile | Humanoids | Drones    │
└─────────────────────────────────────────────────┘

Configuration

Create a config.yaml file:

robot:
  platform: universal_robot
  model: UR5e
  endpoint: 192.168.1.100

perception:
  cameras:
    - name: head_camera
      type: realsense_d435
      resolution: [1280, 720]
      fps: 30

safety:
  workspace:
    x: [0, 2.0]
    y: [-1.0, 1.0]
    z: [0, 1.5]
  max_velocity: 0.5  # m/s
  max_force: 50  # N

agent:
  model: claude-sonnet-4
  api_key: ${ANTHROPIC_API_KEY}

API Reference

Core Classes

`EmbodiedOS`

Main interface to the system.

os = EmbodiedOS(config_path="config.yaml")
robot = os.connect_robot(platform, model, endpoint)
os.disconnect_all()

`Robot`

Represents a connected robot.

robot.actions.move_to(x, y, z)
robot.perception.get_image()
robot.state.get_joint_positions()
robot.safety.emergency_stop()

`AgentInterface`

AI agent control interface.

agent = AgentInterface(model="claude-4", robot=robot)
agent.execute(task_description)
agent.chat(message)

Requirements

Python: 3.9 or higher
Dependencies: numpy>=1.20.0, pyyaml>=6.0
Optional: ROS2 (for ROS integration)
Optional: CUDA (for vision processing)
Optional: Anthropic API key (for Claude agent)
Optional: OpenAI API key (for GPT agent)

Examples

See the examples/ directory:

basic_control.py - Basic robot control
agent_control.py - AI agent interaction

Run examples:

python examples/basic_control.py
python examples/agent_control.py

Documentation

GitHub: https://github.com/ZhenRobotics/openclaw-embodied-os
npm: https://www.npmjs.com/package/openclaw-embodied-os
PyPI: https://pypi.org/project/openclaw-embodied-os/
README: Complete documentation and guides
QUICKSTART: 5-minute quick start guide

Roadmap

Phase 1: Core Platform (Current)

Phase 2: Advanced Features (Q2 2026)

Multi-robot coordination
Advanced vision processing
Learning from demonstration
Cloud deployment

Phase 3: Ecosystem (Q3 2026)

Skill marketplace
Community plugins
Simulation environments
Mobile app control

License

MIT License - see LICENSE file for details.

Changelog

v0.1.0 (2026-03-08)

Initial release
Unified robot control interface across platforms
AI Agent natural language control
Multi-modal perception (vision, audio, tactile)
High-level action primitives
AI-powered task planning
Multi-layer safety system
Support for Universal Robots, Franka Panda, Boston Dynamics Spot
Simulated robot support for testing