Lab ShapeOPT

Parametric gripper design and shape optimization lab, built for the EmioLabs platform.

---

Description

Lab ShapeOPT lets you generate soft robotic gripper geometries from a parameter config and evaluate how well they grasp objects in a SOFA physics simulation. The optimization itself (Optuna + CMA-ES, parallel runSofa scheduling, scoring, live dashboard) is provided by the sofaopt framework; this lab is a sofaopt consumer — it supplies the gripper parameters, the geometry generator, the test scenes and the scoring (see sofaopt_project.py).

---

Installation

Prerequisites: EmioLabs installed (provides SOFA and runSofa.exe). Python 3.10+.

Dependencies are managed by EmioLabs. Additionally, install the optimization framework into the emio-labs bundled Python:

pip install -e path/to/SofaOptimisation[dashboard,preview]

If running outside the platform, also install the packages used across geometry/ and generation/ manually (CadQuery, gmsh, pyvista, matplotlib).

---

Usage

Run through EmioLabs (recommended) — use the provided button in optimisation part of the platform.

Or manually from the terminal:

Generate a gripper mesh from the active config:

python generation/generate_gripper.py

Launch a SOFA simulation scene:

runSofa.exe -l SofaPython3 scenes/lab_shapeOPT_inverse.py

Run the optimization loop:

python optimize.py

Open the dashboard:

python launcher/launch_web.py

Run the unit tests:

python -m pytest

---

Project Structure

lab_shapeOPT/
├── config/            # Active gripper config files (JSONC) read by generation and optimization
├── cool_grippers/     # Curated saved gripper configs with preview images — reference designs
├── dashboard/         # The lab's own dashboard tabs (Generate, Scenes) layered onto sofaopt's dashboard
├── generation/        # Scripts to build a gripper mesh from the active config (standard and fine variants)
├── geometry/          # Parametric geometry engine — part definitions, assembly, mesh export, param schema
├── labtests/          # Registry of composable simulation tests used by the optimizer to score grippers
├── launcher/          # Entry-point scripts — bootstraps the environment and starts the web interface
├── project/           # EmioLabs platform project files (platform-specific format, not Python)
├── runtime/           # Generated at runtime — Optuna DB, session config, trial results
├── scenes/            # SOFA scene scripts passed directly to runSofa.exe
├── tests/             # pytest unit tests for the pure-Python layers
├── names.py           # Single source for cross-component part/file names
├── sofaopt_project.py # The sofaopt adapter: params, tests, SOFA runtime, prepare hook
└── optimize.py        # Headless optimization entry point (dashboard Run button + CLI)

---

Features

• Parametric gripper geometry (~25 parameters: pincer shape, leg dimensions, tilt angles, etc.)
• CMA-ES evolutionary optimization via Optuna — automatic search across generations (provided by sofaopt)
• SOFA simulation integration — each candidate is physically evaluated for grasp success
• Parallel trial execution with process throttling and subprocess cleanup
• Live progress tracking via runtime/trials/progress.json
• Results analysis: ranked leaderboard, score history plot, rolling average and best-so-far trends
• Modular labtest system — composable test scenes (grasp-hold, random cube pick, gripper tilt)

---

Tech Stack

• Python — core language
• CadQuery — parametric CAD geometry
• gmsh — mesh generation (STL/VTK export)
• SOFA Framework — physics-based simulation (installed via EmioLabs)
• sofaopt — optimization framework (Optuna + CMA-ES, parallel runSofa, live dashboard)
• pyvista — offscreen 3D preview rendering
• Dash / matplotlib — results visualization and dashboard