LLM-Controlled Robotic Dog

RoboticsDecember 19, 2025

Autonomous robotic dog controlled through natural language using Claude API and ESP32 microcontroller. Performs complex choreographed sequences including backflips, push-ups, and stretches through conversational commands.

Robotics
Claude
ESP32
Python
Microcontroller
LLM
Hardware
Computer Vision

Key Features

  • Natural language command interpretation using Claude API
  • ESP32 microcontroller serial communication
  • Autonomous multi-step action sequences
  • Gyroscope-based auto-correction for balance
  • Complex choreography engine (backflips, push-ups, stretches)
  • Real-time movement coordination
  • Hardware-software integration with LLM control
  • Conversational robotics interface

This project demonstrates the intersection of large language models and robotics by enabling natural language control of a physical robotic dog. Instead of pre-programmed commands, the robot interprets conversational instructions and executes complex movement sequences.

Architecture

The system bridges AI and hardware through three core components:

  1. Claude API Integration: Processes natural language commands and translates them into action sequences
  2. ESP32 Microcontroller: Handles real-time motor control and sensor feedback
  3. Serial Communication Protocol: Ensures reliable command transmission between Python host and embedded system

Capabilities

Natural Language Control: Give commands like "do three backflips then take a bow" and watch the robot interpret and execute the sequence autonomously.

Gyroscopic Stabilization: Built-in IMU provides auto-correction, enabling the robot to maintain balance during complex maneuvers.

Choreographed Sequences: The robot performs intricate multi-step routines including:

  • Dynamic backflips with landing stabilization
  • Push-up sequences with proper form
  • Stretching movements for demonstration
  • Custom action chains from conversational input

Technical Implementation

The Claude API acts as the "brain," understanding context and intent from natural language. It generates movement primitives that the ESP32 executes through precise servo control. Gyroscope feedback creates a closed-loop system where the robot adjusts movements in real-time.

Demo Video

Watch the full demonstration on YouTube: youtu.be/Xs0VmfZI658

The video showcases natural language command interpretation, autonomous action sequences, and the robot's ability to chain complex movements through conversational interaction.