Research & Data
Sensing at the Edges
Section titled “Sensing at the Edges”Sensing at the Edges shows one way to combine soft-body simulation, distributed control, and evolutionary search. The experiment studies coordination in a body of identical contractile cells whose controllers receive local deformation feedback and neighbouring signals.
- Project overview and animations — start here to see the system and its behaviour.
- Paper PDF — Sensing at the edges: synthetic specimens for early contractile coordination, Ben Gaskin.
- Research code and reproduction notes — the implementation and its documented limitations.
How this uses the simulator
Section titled “How this uses the simulator”Read the example in three layers:
- Simulation: JAX EvoGym supplies the soft-body physics, deformation measurements, and actuator primitives. See Simulation Loop and Observation Helpers.
- Control: the research project supplies an NCA-CTRNN controller: recurrent dynamics within each cell, combined with local communication between cells. It converts feedback into horizontal and vertical actuation and signalling. Contractile cells use per-axis control; the built-in environments’ scalar action interface is not a drop-in replacement.
- Search and analysis: the research project supplies CMA-MAE quality-diversity search, task definitions, fitness, and post-run assays. These are choices made for the experiment, rather than requirements imposed by the simulator. In this experiment, evolution changes the controller parameters while the body stays fixed.
This is an advanced worked application. For a first simulation, begin with First Simulation and Controllers and Actions. Then use the paper and code to follow how sensing, controller state, actuation, and fitness fit together. You can bring a different controller or search algorithm to the same simulator.
Where to look in the code
Section titled “Where to look in the code”The research repository contains:
sensing_edges/— controllers, training, evaluation, task configuration, and terrain suites.preprint/— analysis and figure scripts.s4/— specimen bundles, replay/probe tools, and the validity audit.
Start with a saved specimen and its configuration before attempting a training run. Training uses Modal GPU infrastructure; consult the repository’s reproduction instructions for setup and compute requirements.
Reproduction and evidence limits
Section titled “Reproduction and evidence limits”The repository documents differences between its current training preset and
the settings that produced archived specimens. Use each specimen’s
config_snapshot.yaml and the audit notes when interpreting a replay. Some
resumed runs lack the parent checkpoints needed to regenerate their evolution
from scratch. Specimen replay and training reproduction are distinct tasks.
The paper’s results concern its chosen bodies, controllers, tasks, and selected specimens. They do not establish that every robot or controller built with JAX EvoGym will exhibit the same behaviour. Read the project’s discussion of evidence and limits alongside the simulator’s parity scope.
Full datasets
Section titled “Full datasets”Curated specimen bundles are included in the research repository. Complete run outputs are available on request: open an issue there and identify the figure, specimen, or dataset you need.