Neural Interactive Shaping of Parameter Spaces

https://musicallyembodiedml.github.io/memlnaut/approaches/nisps

Firmware

The hardware firmware targets the MEMLNaut RP2350 build and uses repo-local helper scripts for the known-good build configuration:

git submodule update --init --recursive

scripts/build-firmware.sh
scripts/flash-firmware.sh
scripts/build-and-flash-firmware.sh

Notes:

• The scripts build for rp2040:rp2040:solderparty_rp2350_stamp_xl with Optimize3.
• The build forces C++20 because the firmware uses std::span and concepts.
• build-firmware.sh accepts an optional variant name such as MEMLCelium or BreakOr. Matching remains case-insensitive, so memlcelium still works. If you omit it in an interactive shell, the script parses MEMLNaut-NISPS.ino, prompts for a variant, and rewrites the active MEMLNAUT_MODE_TYPE before building.
• flash-firmware.sh accepts an optional mountpoint argument, or auto-detects common UF2 bootloader mounts such as /run/media/$USER/RP2350 and /run/media/$USER/RPI-RP2.

Web Playground

Try NISPS in your browser — no hardware required:

cd playground
python3 -m http.server
# Open http://localhost:8000

Train a neural network to map joystick positions to generative visuals through interactive machine learning. Two learning modes: direct example mapping and reinforcement learning with thumbs up/down feedback.

The playground UI includes an Expand toggle on the visual surface so you can make the canvas nearly full-screen while compressing parameter/control panels into a minimal strip beneath it.