Add evolved controllers and model card

README.md

@@ -1,3 +1,96 @@
 ---
 license: mit
+tags:
+  - evolutionary-robotics
+  - neuroevolution
+  - emergent-communication
+  - multi-agent
+  - robotics
+  - pybullet
+  - gru
 ---
+
+# essim2d3d controllers
+
+Evolved controller weights for the paper
+[Evaluating the Transfer of Co-Evolved Communication from 2D to 3D Simulation](https://arxiv.org/abs/2610.09280)
+(arXiv:2610.09280).
+
+Code: [github.com/ferdiex/essim2d3d](https://github.com/ferdiex/essim2d3d) ·
+Archive: [Zenodo](https://doi.org/10.5281/zenodo.21967056)
+
+## What these weights are
+
+These are not models in the usual machine learning sense. Each file holds the
+weights of two small recurrent controllers, one per robot (`brain_a` and
+`brain_b`), co-evolved with a genetic algorithm in a two-robot cooperative
+foraging task where one signalling channel can be used to communicate the
+location of food.
+
+Each controller is a GRU network with 16 units and a residual path that
+connects the inputs directly to the outputs:
+
+| Array | Shape | Role |
+|---|---|---|
+| `w_gru` | 27 x 48 | GRU weights (11 inputs + 16 recurrent units, 3 gates x 16) |
+| `b_gru` | 48 | GRU biases |
+| `w_out` | 16 x 5 | GRU state to outputs |
+| `b_out` | 5 | Output biases |
+| `w_res` | 11 x 5 | Residual path, inputs to outputs |
+
+That is 1,484 parameters per robot. The weights are plain JSON lists (no
+PyTorch or other framework) and only make sense together with the simulators,
+sensor encoding and instinct layer in the GitHub repository. They run on CPU;
+no GPU is needed.
+
+## Files
+
+| File | Description |
+|---|---|
+| `social3d_v15_full_final.json` | **Official checkpoint** used for the results reported in the paper (3D, PyBullet) |
+| `social2d_emergence.json` | Controllers co-evolved in the 2D grid simulator |
+| `social2d_*_final.json`, `social3d_*_final.json` | Further independently evolved 2D and 3D checkpoints (see the paper's Methods) |
+| `social3d_v10_*`, `social3d_v14_*` | Earlier 3D development checkpoints |
+| `braitenberg_avoidance.json` | Hand-set Braitenberg obstacle-avoidance baseline (not evolved) |
+
+## How to use
+
+Installation, training, visualization and the commands to reproduce the
+paper's results are documented in the
+[GitHub README](https://github.com/ferdiex/essim2d3d#readme). The same
+checkpoints live in its
+[`models/`](https://github.com/ferdiex/essim2d3d/tree/main/models) folder;
+this repository mirrors them for discoverability on the Hub.
+
+To fetch a checkpoint from here instead:
+
+```python
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="xferdie/essim2d3d-controllers",
+    filename="social3d_v15_full_final.json",
+)
+```
+
+and pass `path` to the repository's viewers (`essim3d.py --controller` for 3D,
+`essim2d.py --model` for 2D).
+
+## Citation
+
+```bibtex
+@misc{montesgonzalez2026transfer2d3d,
+  author        = {Montes-Gonzalez, Fernando},
+  title         = {Evaluating the Transfer of Co-Evolved Communication
+                   from 2D to 3D Simulation},
+  year          = {2026},
+  eprint        = {2610.09280},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.RO},
+  doi           = {10.48550/arXiv.2610.09280}
+}
+```
+
+## License
+
+MIT, as the code repository.

braitenberg_avoidance.json

@@ -0,0 +1,8 @@
+{
+  "encoding": "braitenberg",
+  "left_weights":  [-0.8, -0.6, -0.3, -0.15, 0.15, 0.3, 0.6, 0.8],
+  "right_weights": [0.8, 0.6, 0.3, 0.15, -0.15, -0.3, -0.6, -0.8],
+  "left_bias": 0.3,
+  "right_bias": 0.3,
+  "max_speed": 0.15
+}

social2d_emergence.json

@@ -0,0 +1 @@
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social2d_indep2_final.json

@@ -0,0 +1 @@
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social2d_indep3_final.json

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social2d_sesgo_check1_final.json

@@ -0,0 +1 @@
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social3d_indep2_final.json

@@ -0,0 +1 @@
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social3d_indep3_final.json

@@ -0,0 +1 @@
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social3d_sesgo_check1_150_final.json

@@ -0,0 +1 @@
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social3d_v10_nn_annealing_gen143.json

@@ -0,0 +1 @@
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social3d_v14_bonus_boost_pilot_final.json

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social3d_v15_full_final.json

@@ -0,0 +1 @@
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