openclaw-embodied-os

v0.1.0

Published

8 days ago

A unified operating system for controlling embodied intelligent robots - the control hub bridging AI agents and physical world

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zhenstaff

robotics embodied-ai robot-control ai-agents llm automation physical-ai intelligent-robots robot-os

Embodied-OS

The Control Hub for Embodied Intelligence - Bridging AI Agents and the Physical World

Embodied-OS is a unified operating system that enables AI agents to seamlessly control physical robots and interact with the real world. It provides a standardized interface for perception, reasoning, and action execution across diverse robotic platforms.

Core Vision

Transform how AI interacts with physical reality by providing:

Unified Control Interface: Single API for controlling any robot
AI-Native Design: Built for LLM and agent-based control
Real-time Perception: Multi-modal sensor fusion (vision, audio, touch)
Safe Execution: Built-in safety constraints and human oversight
Plug-and-Play: Easy integration with existing robotic systems

Architecture

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

Quick Start

Installation

# Clone the repository
git clone https://github.com/ZhenRobotics/openclaw-embodied-os.git
cd openclaw-embodied-os

# Install dependencies
pip install -e .
# Or install from PyPI
pip install openclaw-embodied-os

# Configure API keys (for AI agents)
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

Basic Usage

from embodied_os import EmbodiedOS, RobotConfig

# Initialize the OS
os = EmbodiedOS()

# Connect to a robot
robot = os.connect_robot(
    platform="universal_robot",
    model="UR5e",
    endpoint="192.168.1.100"
)

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

# Or programmatic control
result = robot.actions.grasp(
    object_id="red_cube",
    approach_vector=[0, 0, -1]
)

Agent Integration

from embodied_os import AgentInterface

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

# The agent can now control the robot through natural language
agent.chat("Look around the room and tell me what you see")
agent.execute("Navigate to the kitchen and open the fridge")

Core Components

1. Robot Abstraction Layer (RAL)

Provides a unified interface across different robot platforms:

Motion Control: Move, rotate, grasp, release
Perception: Camera, LiDAR, IMU, force sensors
State Management: Joint positions, velocities, forces
Safety: Collision detection, emergency stop, workspace limits

Supported Platforms:

Universal Robots (UR3e, UR5e, UR10e)
Franka Emika Panda
Boston Dynamics Spot
Custom robots via plugin system

2. Perception System

Multi-modal sensor processing:

# Visual perception
objects = robot.perception.detect_objects()
depth_map = robot.perception.get_depth_map()

# Audio perception
audio_stream = robot.perception.listen()
transcription = robot.perception.transcribe(audio_stream)

# Tactile sensing
contact_force = robot.perception.get_contact_force()

3. Action Executor

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", position=[0.5, 0.3, 0])

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

4. Task Planner

AI-powered task decomposition:

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

# Automatic decomposition
plan = robot.planner.create_plan(task)
# Returns:
# 1. Navigate to kitchen
# 2. Locate coffee machine
# 3. Pick up cup
# 4. Place cup under dispenser
# 5. Press brew button
# 6. Wait for brewing
# 7. Pick up cup
# 8. Navigate to user
# 9. Hand over cup

# Execute with monitoring
robot.planner.execute(plan, monitor=True)

5. Safety System

Multi-layer safety guarantees:

# 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
)

# Collision avoidance
robot.safety.enable_collision_avoidance(
    objects=["table", "wall", "human"]
)

# Force limits
robot.safety.set_max_force(newtons=50)

# Emergency stop
robot.safety.set_emergency_stop_callback(on_emergency)

Use Cases

1. Warehouse Automation

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

# Agent-driven inventory management
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")

# Proactive assistance
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.
    If they seem distressed, alert the caregiver.
""")

3. Research Lab Assistant

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

# Complex manipulation
agent.execute("""
    Set up the chemistry experiment:
    1. Measure 50ml of solution A into beaker
    2. Heat to 60 degrees Celsius
    3. Add 2 drops of catalyst
    4. Stir for 2 minutes
    5. Transfer to test tube
""")

Features

Agent-First Design

Natural Language Control: Speak to robots like you speak to humans
Multi-Agent Coordination: Multiple agents controlling multiple robots
Context Awareness: Robots understand their environment and task context
Learning from Feedback: Improve performance based on corrections

Cross-Platform Support

Hardware Agnostic: Works with any robot via adapters
ROS/ROS2 Integration: Seamless integration with ROS ecosystem
Simulation Support: Test in Gazebo, Isaac Sim, MuJoCo
Cloud and Edge: Deploy on cloud servers or embedded devices

Developer Friendly

Python SDK: Intuitive API for rapid development
TypeScript SDK: Web-based control interfaces
REST API: HTTP endpoints for any language
WebSocket: Real-time bidirectional communication

Production Ready

Monitoring Dashboard: Real-time robot status and metrics
Logging System: Comprehensive action and event logs
Error Recovery: Automatic retry and fallback mechanisms
Fleet Management: Control and monitor multiple robots

Installation

Prerequisites

Python 3.9+
(Optional) ROS2 Humble or later
(Optional) CUDA for vision processing

From PyPI

pip install openclaw-embodied-os

From Source

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

Hardware Setup

Connect your robot to the network and configure the endpoint:

# For Universal Robots
embodied-os connect --platform ur --model ur5e --ip 192.168.1.100

# For custom robots
embodied-os connect --platform custom --config robot_config.yaml

Configuration

Create a configuration file config.yaml:

robot:
  platform: universal_robot
  model: UR5e
  endpoint: 192.168.1.100

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

  lidar:
    enabled: true
    type: velodyne_vlp16

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
  collision_check: true

agent:
  model: claude-sonnet-4
  api_key: ${ANTHROPIC_API_KEY}
  temperature: 0.7
  max_retries: 3

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()

`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)

Examples

See the examples/ directory for complete working examples:

examples/basic_control.py - Basic robot control
examples/agent_navigation.py - Agent-driven navigation
examples/multi_robot.py - Coordinate multiple robots
examples/vision_manipulation.py - Vision-guided manipulation
examples/human_interaction.py - Human-robot interaction

Development

Running Tests

pytest tests/

Building Documentation

cd docs
make html

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Roadmap

Phase 1: Core Platform (Current)

[x] Robot abstraction layer
[x] Basic perception system
[x] Action executor
[x] Safety system
[ ] Agent interface

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

Community

GitHub: https://github.com/ZhenRobotics/openclaw-embodied-os
Discord: https://discord.gg/embodied-os
Documentation: https://docs.embodied-os.ai
Blog: https://blog.embodied-os.ai

License

MIT License - see LICENSE file for details.

Citation

If you use Embodied-OS in your research, please cite:

@software{embodied_os_2026,
  title = {Embodied-OS: A Unified Operating System for Embodied Intelligence},
  author = {ZhenRobotics Team},
  year = {2026},
  url = {https://github.com/ZhenRobotics/openclaw-embodied-os}
}

Acknowledgments

Built with inspiration from:

ROS (Robot Operating System)
OpenAI Robotics
Boston Dynamics AI Institute
The open-source robotics community

Embodied-OS - Making robots as easy to control as talking to a friend.

Published

Vulnerabilities

Links

Maintainers

Keywords

Readme

Embodied-OS

Core Vision

Architecture

Quick Start

Installation

Basic Usage

Agent Integration

Core Components

1. Robot Abstraction Layer (RAL)

2. Perception System

3. Action Executor

4. Task Planner

5. Safety System

Use Cases

1. Warehouse Automation

2. Elderly Care Assistant

3. Research Lab Assistant

Features

Agent-First Design

Cross-Platform Support

Developer Friendly

Production Ready

Installation

Prerequisites

From PyPI

From Source

Hardware Setup

Configuration

API Reference

Core Classes

EmbodiedOS

Robot

AgentInterface

Examples

Development

Running Tests

Building Documentation

Contributing

Roadmap

Phase 1: Core Platform (Current)

Phase 2: Advanced Features (Q2 2026)

Phase 3: Ecosystem (Q3 2026)

Community

License

Citation

Acknowledgments

`EmbodiedOS`

`Robot`

`AgentInterface`