Workspace Analyzer Samplers

Workspace Analyzer Samplers#

The samplers module provides various sampling strategies for workspace analysis, from uniform grids to quasi-random sequences.

Table of Contents#

Quick Start#

import torch
from embodichain.lab.devices.workspace_analyzer.samplers import UniformSampler

# Define sampling bounds
bounds = torch.tensor([[0.0, 1.0],    # x-axis range
                       [0.0, 1.0]])   # y-axis range

# Create uniform sampler and generate 1000 samples
sampler = UniformSampler(samples_per_dim=10)
samples = sampler.sample(1000, bounds)  # Note: (num_samples, bounds) order

Overview#

Available sampling strategies:

  • UniformSampler: Grid-based regular sampling ✅

  • RandomSampler: Pure random sampling ✅

  • GaussianSampler: Normal distribution sampling ✅

Basic Usage#

import torch
import numpy as np
from embodichain.lab.sim.utility.workspace_analyzer.samplers import (
    UniformSampler, RandomSampler, GaussianSampler
)

# Define bounds: [[min, max], [min, max], ...]
bounds = torch.tensor([[-1, 1], [-1, 1], [0, 2]], dtype=torch.float32)

# Generate samples with implemented samplers
samples = UniformSampler(samples_per_dim=10).sample(1000, bounds)
print(f"Generated {samples.shape[0]} samples")

Available Samplers#

1. UniformSampler#

Grid-based sampling with regular spacing.

import torch
from embodichain.lab.sim.utility.workspace_analyzer.samplers import UniformSampler

bounds = torch.tensor([[-1, 1], [-1, 1], [0, 2]], dtype=torch.float32)
# Create uniform sampler
uniform_sampler = UniformSampler(seed=42, samples_per_dim=10)
samples = uniform_sampler.sample(1000, bounds)

Best for: Low-dimensional spaces (2-4D), systematic exploration

2. RandomSampler#

Pure random sampling with uniform distribution.

from embodichain.lab.sim.utility.workspace_analyzer.samplers import RandomSampler

random_sampler = RandomSampler(seed=42)
samples = random_sampler.sample(1000, bounds)

Best for: High-dimensional spaces, baseline comparisons

3. GaussianSampler ✅#

Gaussian distribution sampling around specified mean.

from embodichain.lab.sim.utility.workspace_analyzer.samplers import GaussianSampler

gaussian_sampler = GaussianSampler(seed=42, std=0.2)
samples = gaussian_sampler.sample(1000, bounds)

Best for: Uncertainty analysis, robustness studies around workspace center

Factory Pattern Usage#

Use the factory to create samplers by strategy (only implemented samplers):

from embodichain.lab.sim.utility.workspace_analyzer.samplers import create_sampler
from embodichain.lab.sim.utility.workspace_analyzer.configs import SamplingStrategy

# Create implemented samplers by strategy
uniform_sampler = create_sampler(
    SamplingStrategy.UNIFORM, 
    seed=42, 
    samples_per_dim=10
)

random_sampler = create_sampler(
    SamplingStrategy.RANDOM,
    seed=42
)

gaussian_sampler = create_sampler(
    SamplingStrategy.GAUSSIAN,
    seed=42,
    std=0.2
)

Integration with Workspace Analyzer#

from embodichain.lab.sim.utility.workspace_analyzer.configs import SamplingConfig, SamplingStrategy

# Configure sampling in analyzer
sampling_config = SamplingConfig(
    strategy=SamplingStrategy.UNIFORM,
    num_samples=1000,
    seed=42
)

# Analyzer will use the specified sampler automatically

Quick Reference#

Available Samplers:

  • UniformSampler: Use for 2-4D systematic exploration

  • RandomSampler: Baseline for any dimensional spaces

  • GaussianSampler: Uncertainty and robustness analysis

Sample Size Guidelines:

  • Uniform: samples_per_dim^n_dims (exponential growth)

  • Random: Any size (flexible)

  • Gaussian: Any size (flexible)

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