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hwihwalab/
pusher-v5-ppo

Name: pusher-v5-ppo
Rating: 2.5 (156 reviews)

stable-baselines3

reinforcement-learning

deep-reinforcement-learning

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🦾 Pusher-v5 PPO // AI Hub & Live Control Cockpit

GitHub Repository

License: MIT

PyTorch

MuJoCo 7-DOF Robotic Continuous Control Telemetry & PPO Deep Reinforcement Learning Platform
🌐 English Documentation | 🇰🇷 한국어 매뉴얼

This repository contains an advanced continuous deep reinforcement learning system (PPO) and a real-time engineering telemetry cockpit for 7-DOF robotic arm manipulation in Gymnasium MuJoCo Pusher-v5.

🌟 Model Specifications & Benchmark Performance

Parameter	Specification
Environment	Gymnasium MuJoCo `Pusher-v5` (7-DOF Robotic Arm)
Observation Space	23-dimensional continuous vector (Joints, Velocities, Tip 3D, Object 3D, Goal 3D)
Action Space	7-dimensional continuous motor torques (`Box[-2.0, 2.0]`, float32)
Algorithm	Proximal Policy Optimization (PPO) with `MlpPolicy`
Deep Learning Framework	Stable-Baselines3 / PyTorch backend
Baseline Return (Step 0)	`-57.51 pts` (Random exploration, arm-to-object dist ~0.215m)
Converged Return (Step 300k+)	`-32.42 ± 4.30 pts` (Peak: `-26.15 pts`)
Arm-to-Object Proximity	`0.028 m` (Precise contact & cylinder grasp alignment)
Goal Proximity Accuracy	`0.054 m` (Target zone reached & pushed)

🏛️ System Architecture

mermaid

flowchart TD
    subgraph Web_Cockpit ["1-Screen Zero-Scroll Robotics Telemetry Cockpit"]
        W1["HTML5 / CSS3 / Vanilla JS Client"] <-->|"WebSocket /ws/simulation @ 30 FPS"| S1["FastAPI High-Performance Engine"]
        S1 -->|"Base64 JPEG Physics Stream"| W1
        S1 -->|"7-DOF Bipolar Torques (-2 to +2 Nm)"| W1
        S1 -->|"3D Vector Coordinates (Tip, Obj, Goal)"| W1
        W1 -->|"Control Commands (Start, Pause, Step, Reset, Policy)"| S1
    end

    subgraph Analytics_Deck ["4-Tab Analytics & Replay Deck"]
        T1["Tab 1: Live Telemetry Dynamics (Raw & 20-Ep Moving Average)"]
        T2["Tab 2: Milestone Replay Deck (16:9 Widescreen Video Gallery)"]
        T3["Tab 3: Live PPO Logs (Algorithmic Console Stream)"]
        T4["Tab 4: Environment & Reward Math Specifications"]
    end

    subgraph Deep_RL_Pipeline ["Stable-Baselines3 PPO Training Loop"]
        TR1["train.py / Background Thread"] --> TR2["MuJoCo Pusher-v5 Physics"]
        TR2 --> TR3["VisualProgressCallback"]
        TR3 --> TR4["Step 0 to 300k MP4 & GIF Videos"]
        TR3 --> TR5["Training Plots & Metrics JSON"]
        TR4 & TR5 --> TR6["Single-Click ZIP Archive: ppo_pusher_bundle.zip"]
    end

🕹️ Interactive Cockpit Features

High-Fidelity 30 FPS Physics Stream:
- Ultra low-latency canvas streaming via WebSocket.
- 7-DOF Action Space Motor Torque Bipolar Gauge ([-2.0, +2.0] Nm) with positive (Cyan) and negative (Rose) deflection.
- 3D Cartesian coordinates tracker for Fingertip, Object, and Goal in real meters.
Deep RL Training Budget Presets:
- 500 Ep (50k Steps • ~12s) - Quick Test
- 2,000 Ep (200k Steps • ~45s) - Basic Pushing
- 5,000 Ep (500k Steps • ~1.8m) ★ Recommended Mature
- 10,000 Ep (1M Steps • ~3.5m) - High-Precision
Widescreen Checkpoint Replay Gallery:
- Side-by-side comparative video cards displaying the robotic arm's learning trajectory from random exploration (Step 0) to mature convergence.
- Instant 1-click export for MP4 videos and animated GIFs.
Single-Click ZIP Packaging:
- One-click bundle download (ppo_pusher_bundle.zip) containing weights, milestone videos, and telemetry charts.

🚀 Quickstart & Usage

1. Installation

bash

git clone https://github.com/Hwihwa-Lab/pusher-v5-ppo.git
cd pusher-v5-ppo
pip install -r requirements.txt

2. Launch Local Web Control Cockpit

python app.py

Open your browser at http://localhost:8000.

3. Standalone CLI Training & Evaluation

bash

# Train PPO agent
python train.py --timesteps 300000 --eval_freq 30000

# Evaluate trained model
python evaluate.py --model_path ./results/ppo_pusher.zip --episodes 5

🐍 Quick Python Evaluation Snippet

You can load and evaluate this pre-trained agent in 5 lines of Python using Stable-Baselines3:

python

import gymnasium as gym
from stable_baselines3 import PPO

# 1. Initialize Pusher-v5 environment & load model
env = gym.make("Pusher-v5", render_mode="human")
model = PPO.load("results/ppo_pusher.zip")

# 2. Run deterministic pushing evaluation
obs, _ = env.reset()
done = False
while not done:
    action, _ = model.predict(obs, deterministic=True)
    obs, reward, terminated, truncated, _ = env.step(action)
    done = terminated or truncated

env.close()

⌨️ Keyboard Shortcuts Reference

Key	Action	Description
`Space`	Start / Pause	Toggle 30 FPS MuJoCo physical simulation stream
`R`	Reset Environment	Reset robotic arm, cylinder object, and target goal to new random positions
`S`	Step Once	Advance physics engine forward by 1 discrete timestep (0.05s)
`H`	Toggle HUD	Show or hide on-canvas telemetry data overlay

📂 Repository Contents

README.md: English Model Card and benchmark performance guide.
README_KR.md: Full Korean comprehensive manual (한국어 매뉴얼).
app.py: FastAPI high-performance backend & 30 FPS WebSocket simulation server.
train.py: Stable-Baselines3 PPO 7-DOF training engine with VisualProgressCallback.
evaluate.py: Standalone 5-episode deterministic policy evaluator and video recorder.
visualizer.py: Standalone Matplotlib visualizer and benchmark plotter.
web/: 1-Screen zero-scroll telemetry cockpit frontend (app.js, index.html, style.css).
results/ppo_pusher.zip: Pre-trained PPO neural network weights (300,000 steps, -32.4 pts).
ppo_pusher_bundle.zip: Complete production archive with weights, 12 checkpoint videos, and plots.
deploy_to_hf.py: One-click automated Hugging Face Model Hub deployer.
requirements.txt & packages.txt: Python and system dependency manifests.

🔗 Open Source Hubs & Project Links

🐙 GitHub Repository: https://github.com/Hwihwa-Lab/pusher-v5-ppo
🤗 Hugging Face Model Hub: https://huggingface.co/hwihwalab/pusher-v5-ppo

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Trained and deployed with Pusher AI Hub by hwihwalab.

hwihwalab/pusher-v5-ppo