160 lines
7.1 KiB
Markdown
160 lines
7.1 KiB
Markdown
# JackBot — Hexapod Control, Simulation & RL Framework
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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.
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---
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## Key Features
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* **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.
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* **Flexible Input Pipeline:** Pluggable input handlers supporting Pygame gamepad controllers, manual GUI sliders, or randomized direction vectors.
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* **Parallel Multi-Robot Training:** Vectorized Gymnasium environment (`JackBotEnv`) capable of simulating and training $N$ parallel hexapods simultaneously in PyBullet for PPO reinforcement learning.
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* **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).
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---
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## Project Architecture
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```text
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JackBot/
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├── main.py # Primary application entry point for manual & hardware control
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├── Robot.py # Core Robot class, kinematics wrapper, and Backend protocols
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├── config.py # Global settings (backend selection, URDF path, communication specs)
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├── kinematics.py # Forward and Inverse Kinematics (IKPy)
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├── simulation.py # Base PyBullet GUI wrapper for single-robot interactive simulation
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├── robot_init.py # Default stance angles and neutral leg positions
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├── DataTypes.py # Strongly typed arrays (PosArray, RadArray, DegArray) & structs
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│
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├── states/ # Finite State Machine (FSM) gait states
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│ ├── State.py # Base State class
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│ ├── idle.py # Neutral stance state
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│ └── walking.py # Inverse-kinematics tripod gait state
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│
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├── inputs/ # Input providers
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│ ├── InputProvider.py # Base input abstraction
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│ └── PygameController.py # Asynchronous gamepad loop (process-isolated)
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│
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├── gui/ # Control interface
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│ └── MainWindow.py # Pygame / parameter GUI layout and command resolver
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│
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├── EspCommunication.py # WiFi socket sender for ESP32 hardware
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├── ArduinoCommunication.py # Serial communication wrapper for Arduino hardware
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├── JackBotUrdf.urdf # Kinematic 3D model definition (18 active joints)
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│
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└── ml/ # Machine Learning Subsystem
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├── env.py # JackBotEnv (Gymnasium multi-robot vector environment)
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├── SimManager.py # Physics server initialization and scene loading
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├── MetricsOverlay.py # PyBullet HUD (MetricsHUD) & leader crown tracking (LeaderCrown)
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├── run_train.py # PPO training execution script
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└── run_eval.py # Model evaluation script
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```
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---
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## System Requirements
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* **Python 3.12** (Recommended)
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* **OS:** Linux (Ubuntu/Debian) or Windows 10/11
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* **Dependencies:** `pybullet`, `gymnasium`, `stable-baselines3`, `torch`, `numpy`, `pygame`, `ikpy`, `pyserial`, `matplotlib`
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---
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## Installation & Setup
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### Linux (Bash)
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```bash
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python3.12 -m venv .venv
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source .venv/bin/activate
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python -m pip install --upgrade pip setuptools wheel
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pip install -r requirements.txt
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```
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> **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:
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> ```bash
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> export DISPLAY=:0
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> python main.py
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> ```
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### Windows (PowerShell)
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```powershell
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python3.12 -m venv .venv
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.\.venv\Scripts\Activate.ps1
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python -m pip install --upgrade pip setuptools wheel
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pip install -r requirements.txt
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```
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If PowerShell blocks script execution:
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```powershell
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Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned
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.\.venv\Scripts\Activate.ps1
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```
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---
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## Usage Guide
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### 1. Manual Control & Hardware Streaming (`main.py`)
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`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.
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To launch:
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```bash
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python main.py
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```
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#### Configuration (`config.py`)
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Edit `config.py` prior to launching `main.py` to configure execution mode and connections:
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* **Backend Selection (`cfg.backend`):**
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* `BackendType.SIMULATION`: Executes motion inside a 3D PyBullet window.
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* `BackendType.ESP32`: Streams target joint angles over WiFi sockets to an ESP32 micro-controller (`cfg.esp32_ip`, `cfg.esp32_port`).
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* `BackendType.ARDUINO`: Streams target joint angles over Serial to an Arduino (`cfg.port`, `cfg.baudrate`).
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---
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### 2. Machine Learning: PPO Training & Evaluation (`ml/`)
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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 $[-1, 1]$.
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#### A. Training a Model (`run_train.py`)
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Train a single robot policy:
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```bash
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python ml/run_train.py --timesteps 100000 --model ml/checkpoints/ppo_joint_command
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```
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Train using multi-robot parallel vectorization with visual GUI enabled:
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```bash
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python ml/run_train.py --timesteps 500000 --model ml/checkpoints/ppo_joint_command --num-robots 16 --robot-spacing 0.75 --gui
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```
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**Training CLI Arguments:**
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* `--timesteps`: Total training timesteps.
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* `--num-robots`: Number of parallel robot instances spawned in a grid layout (e.g., 16 to 64).
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* `--robot-spacing`: Distance in meters between robot spawn origins.
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* `--start-pose`: Stance pose at environment reset (`init_deg` or `init90_deg`).
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* `--gui`: Renders the live PyBullet GUI with metrics HUD, leader crown, and failure graying.
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#### B. Evaluating a Model (`run_eval.py`)
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Run an evaluation loop using a saved model checkpoint:
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```bash
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python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 5 --gui
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```
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Multi-robot evaluation with custom stance pose:
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```bash
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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
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```
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---
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## Environment Mechanics & Telemetry (`ml/env.py`)
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When training with `--gui`, `JackBotEnv` includes dynamic visual feedback mechanisms:
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* **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**.
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* **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.
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* **Termination Threshold:** The environment episode terminates automatically when the percentage of failed robots exceeds the configured threshold (default: $30\%$). |