AMP_for_hardware

🥈Silver

AMP for Hardware enables operations teams to automate complex hardware tasks using adversarial motion priors. It benefits engineers by reducing the need for intricate reward functions, streamlining robotic control and automation workflows.

7411280Updated Today

Intermediate30min to implementautomation

Saves ~300 min per use

Quick InstallView Source

git clone https://github.com/escontra/AMP_for_hardware.git

Works with:

Claude

Overview

About This Skill

https://bit.ly/3hpvbD6

Use Cases

Train legged robots to navigate complex terrains using minimal reference data.

Implement adversarial motion priors to enhance robotic control without complex reward functions.

Evaluate and visualize the performance of trained policies in simulated environments.

Integrate with Isaac Gym for high-performance robotic simulations and training.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/escontra/AMP_for_hardware

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Generate a Python script using the AMP_for_hardware method to train a robotic arm to perform a complex task like picking and placing objects. The script should include data loading, model architecture, and training loop. Use [DATASET] as the input dataset and [TASK] as the specific task to be performed.

Example Output

```python
# Adversarial Motion Priors for Robotic Arm Control
# Dataset: [DATASET]
# Task: [TASK]

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# Define the Generator and Discriminator models
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.Linear(100, 256),
            nn.ReLU(True),
            nn.Linear(256, 256),
            nn.ReLU(True),
            nn.Linear(256, 10),
            nn.Tanh()
        )

    def forward(self, input):
        return self.main(input)

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Linear(10, 256),
            nn.ReLU(True),
            nn.Linear(256, 256),
            nn.ReLU(True),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input)

# Training parameters
batch_size = 64
lr = 0.0002
num_epochs = 1000

# Initialize models, loss function, and optimizers
generator = Generator()
discriminator = Discriminator()
criterion = nn.BCELoss()
optimizerG = optim.Adam(generator.parameters(), lr=lr)
optimizerD = optim.Adam(discriminator.parameters(), lr=lr)

# Load dataset
dataloader = DataLoader([DATASET], batch_size=batch_size, shuffle=True)

# Training loop
for epoch in range(num_epochs):
    for i, data in enumerate(dataloader):
        # Update Discriminator
        optimizerD.zero_grad()
        real_data = data
        batch_size = real_data.size(0)
        label = torch.full((batch_size,), 1., dtype=torch.float, device=device)
        output = discriminator(real_data).view(-1)
        errD_real = criterion(output, label)
        errD_real.backward()
        D_x = output.mean().item()

        noise = torch.randn(batch_size, 100, device=device)
        fake = generator(noise)
        label.fill_(0.)
        output = discriminator(fake.detach()).view(-1)
        errD_fake = criterion(output, label)
        errD_fake.backward()
        D_G_z1 = output.mean().item()
        errD = errD_real + errD_fake
        optimizerD.step()

        # Update Generator
        optimizerG.zero_grad()
        label.fill_(1.)
        output = discriminator(fake).view(-1)
        errG = criterion(output, label)
        errG.backward()
        D_G_z2 = output.mean().item()
        optimizerG.step()

        # Print training stats
        if i % 50 == 0:
            print('[%d/%d][%d/%d]	Loss_D: %.4f	Loss_G: %.4f	D(x): %.4f	D(G(z)): %.4f / %.4f'
                  % (epoch, num_epochs, i, len(dataloader),
                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
```