Introducing IntelliH1

Intelligence.
From First Principles.

A multi-layer cognitive framework for the Unitree H1. Combining LLM reasoning with robust RL control.

SYSTEM INITIALIZED

Four Layers. One Mind.

LLM Navigation

"Go to the kitchen."

Powered by the Groq API. IntelliH1 parses natural language commands, understands context, and translates intent into precise coordinate targets.

> Parsing command...
> Target identified: "Kitchen"
> Coordinates: [4.5, -2.1, 0.0]
> Executing path planning...
RL Motion

Unitree RL Control.

Official Unitree walking controller. Stable, adaptive, and robust.

Stability 30s+ Continuous
Speed 0.5 - 3.0 m/s
Perception

C++ Optimized.

Real-time LIDAR simulation processing 360° coverage in under 10ms.

Path Planning

A* Navigation.

Dynamic occupancy grid mapping with 0.3m resolution. It doesn't just walk; it knows where to walk.

Cognitive Architecture

Layer 1: High Level

LLM Planning

Uses Llama 3.3 (via Groq) to deconstruct complex user requests into actionable goals. Acts as the creative brain of the operation.

Layer 2: Sensing

C++ Perception

Processes raw LIDAR data into a point cloud representation, identifying obstacles and free space in milliseconds.

Layer 3: Strategy

A* Path Planner

Generates optimal waypoints on a dynamic grid, ensuring collision-free trajectories towards the target.

Layer 4: Execution

RL Motion Control

Executes the physical movement. Adjusts gait, balance, and velocity in real-time to follow the path.

Technical Specifications

Language
Python 3.10+
Physics Engine
MuJoCo 3.3+
Robot Platform
Unitree H1
Inference
Groq API
Core
C++ Bindings
License
MIT