Files

T

2026-07-31 14:50:59 +02:00

7.1 KiB

Raw Blame History

JackBot — Hexapod Control, Simulation & RL Framework

JackBot is a modular 3D hexapod robot control and machine learning framework built in Python. It supports real-time kinematics, multi-input options (GUI, gamepads), hardware streaming (ESP32 / Arduino), and vectorized Reinforcement Learning (PPO) using PyBullet and Gymnasium.

Key Features

Unified Robot Abstraction (Robot.py): Virtual backends (RobotBackend protocol) allow seamless switching between 3D PyBullet simulation and physical hardware (ESP32 / Arduino) without changing high-level logic.
Flexible Input Pipeline: Pluggable input handlers supporting Pygame gamepad controllers, manual GUI sliders, or randomized direction vectors.
Parallel Multi-Robot Training: Vectorized Gymnasium environment (JackBotEnv) capable of simulating and training N parallel hexapods simultaneously in PyBullet for PPO reinforcement learning.
Live Telemetry & Visual Tracking: Built-in PyBullet overlay features including real-time performance HUDs, floating leader crown tracking (\text{👑}) for top-reward robots, and visual failure feedback (failed robots turn semi-transparent dark gray).

Project Architecture

JackBot/
├── main.py                  # Primary application entry point for manual & hardware control
├── Robot.py                 # Core Robot class, kinematics wrapper, and Backend protocols
├── config.py                # Global settings (backend selection, URDF path, communication specs)
├── kinematics.py            # Forward and Inverse Kinematics (IKPy)
├── simulation.py            # Base PyBullet GUI wrapper for single-robot interactive simulation
├── robot_init.py            # Default stance angles and neutral leg positions
├── DataTypes.py             # Strongly typed arrays (PosArray, RadArray, DegArray) & structs
│
├── states/                  # Finite State Machine (FSM) gait states
│   ├── State.py             # Base State class
│   ├── idle.py              # Neutral stance state
│   └── walking.py           # Inverse-kinematics tripod gait state
│
├── inputs/                  # Input providers
│   ├── InputProvider.py     # Base input abstraction
│   └── PygameController.py  # Asynchronous gamepad loop (process-isolated)
│
├── gui/                     # Control interface
│   └── MainWindow.py        # Pygame / parameter GUI layout and command resolver
│
├── EspCommunication.py      # WiFi socket sender for ESP32 hardware
├── ArduinoCommunication.py  # Serial communication wrapper for Arduino hardware
├── JackBotUrdf.urdf         # Kinematic 3D model definition (18 active joints)
│
└── ml/                      # Machine Learning Subsystem
    ├── env.py               # JackBotEnv (Gymnasium multi-robot vector environment)
    ├── SimManager.py        # Physics server initialization and scene loading
    ├── MetricsOverlay.py    # PyBullet HUD (MetricsHUD) & leader crown tracking (LeaderCrown)
    ├── run_train.py         # PPO training execution script
    └── run_eval.py          # Model evaluation script

System Requirements

Python 3.12 (Recommended)
OS: Linux (Ubuntu/Debian) or Windows 10/11
Dependencies: pybullet, gymnasium, stable-baselines3, torch, numpy, pygame, ikpy, pyserial, matplotlib

Installation & Setup

Linux (Bash)

python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

Linux X11 Headless Note: If running PyBullet GUI on Linux gives an X11 server connection error (cannot connect to X server), ensure your display environment variable is set:
export DISPLAY=:0
python main.py

Windows (PowerShell)

python3.12 -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

If PowerShell blocks script execution:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned
.\.venv\Scripts\Activate.ps1

Usage Guide

1. Manual Control & Hardware Streaming (`main.py`)

main.py is the operational entry point for driving the robot manually via GUI sliders or a gamepad, running either in 3D PyBullet simulation or connected to physical hardware.

To launch:

python main.py

Configuration (`config.py`)

Edit config.py prior to launching main.py to configure execution mode and connections:

Backend Selection (cfg.backend):
- BackendType.SIMULATION: Executes motion inside a 3D PyBullet window.
- BackendType.ESP32: Streams target joint angles over WiFi sockets to an ESP32 micro-controller (cfg.esp32_ip, cfg.esp32_port).
- BackendType.ARDUINO: Streams target joint angles over Serial to an Arduino (cfg.port, cfg.baudrate).

2. Machine Learning: PPO Training & Evaluation (`ml/`)

The ml/ directory contains tools to train RL policies using Proximal Policy Optimization (PPO). The agent receives observations (18\text{ joint angles} + 4\text{ velocity/turning commands}) and outputs continuous joint delta actions in [-1, 1].

A. Training a Model (`run_train.py`)

Train a single robot policy:

python ml/run_train.py --timesteps 100000 --model ml/checkpoints/ppo_joint_command

Train using multi-robot parallel vectorization with visual GUI enabled:

python ml/run_train.py --timesteps 500000 --model ml/checkpoints/ppo_joint_command --num-robots 16 --robot-spacing 0.75 --gui

Training CLI Arguments:

--timesteps: Total training timesteps.
--num-robots: Number of parallel robot instances spawned in a grid layout (e.g., 16 to 64).
--robot-spacing: Distance in meters between robot spawn origins.
--start-pose: Stance pose at environment reset (init_deg or init90_deg).
--gui: Renders the live PyBullet GUI with metrics HUD, leader crown, and failure graying.

B. Evaluating a Model (`run_eval.py`)

Run an evaluation loop using a saved model checkpoint:

python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 5 --gui

Multi-robot evaluation with custom stance pose:

python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 3 --gui --num-robots 4 --robot-spacing 0.8 --start-pose init_deg

Environment Mechanics & Telemetry (`ml/env.py`)

When training with --gui, JackBotEnv includes dynamic visual feedback mechanisms:

Failure Detection & Graying: Robots are continuously evaluated for roll/pitch tilt (> 0.7\text{ rad}) or base collapse (< 0.05\text{ m} height). When a robot fails, its state mask is flagged and its 3D mesh automatically turns semi-transparent dark gray.
Leader Crown (\text{👑}): A floating crown indicator tracks and sits directly above the robot currently achieving the highest cumulative reward in the multi-robot grid.
Termination Threshold: The environment episode terminates automatically when the percentage of failed robots exceeds the configured threshold (default: 30\%).

7.1 KiB Raw Blame History