dexsim visualize for per point yoshikawa score

[matafela]

Description

Add workspace manipulability visualization with DexSim support

Visualize per-point Yoshikawa manipulability across the robot workspace, with linear/log color mapping and distinct styling for unreachable samples. Add selected-point diagnostics and translational velocity ellipsoids in Matplotlib.

Include a simulation example that creates a UR5 and displays its colored workspace alongside the robot in DexSim or Viser, with offscreen image export support.

Run the example from the repository root:

conda activate embodichain2

# Open the native DexSim viewer
python -m scripts.tutorials.sim.visualize_robot_workspace

# Use the Viser browser viewer
python -m scripts.tutorials.sim.visualize_robot_workspace --backend viser

# Analyze Cartesian reachability and save an offscreen image
python -m scripts.tutorials.sim.visualize_robot_workspace \
    --mode cartesian_space --headless

Optional flags: --num-samples 6000 and --log-scale.

Type of change

• Enhancement (non-breaking change which improves an existing functionality)

Checklist

• I have run the black . command to format the code base.
• I reviewed affected documentation and agent context, updated it where needed, or explained why no update was needed.
• Public API changes are reflected in the API docs (python docs/scripts/check_api_docs.py), if applicable
• I have added tests that prove my fix is effective or that my feature works
• Dependencies have been updated, if applicable.

[greptile-apps]

Confidence Score: 5/5

The PR appears safe to merge; no concrete behavioral, security, or repository-rule violations remain.

Summary

This PR adds a runnable UR5 workspace visualization tutorial that analyzes manipulability, aligns scores with reachable and rejected samples, and publishes the resulting colored point cloud through native DexSim or Viser.

• Supports joint-space and Cartesian-space analysis with linear or logarithmic color mapping.
• Supports native interactive viewing, browser visualization, and offscreen PNG capture.
• Adds focused tests for score alignment, arena translation, backend routing, lifecycle ordering, and cleanup.
• Updates robot-workspace context documentation to describe the new tutorial and coordinate-frame behavior.

Diagram

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A[Parse tutorial options] --> B[Create SimulationManager]
    B --> C[Add and prepare UR5]
    C --> D[Run WorkspaceAnalyzer]
    D --> E[Align points, scores, and reachability]
    E --> F[Map Yoshikawa scores to RGB]
    F --> G{Selected backend}
    G -->|DexSim| H[Create native point cloud]
    G -->|Viser| I[Publish point-cloud overlay]
    H --> J{Headless?}
    J -->|Yes| K[Render and save PNG]
    J -->|No| L[Open native viewer and update]
    I --> M[Serve browser viewer and update]

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_{Reviews (1) · Last reviewed commit: "update"}

[Yuan-Xinyi]

ready to merge