AMP Tadpole v0.1.0 (preview)
Browse files- LICENSE +408 -0
- LICENSE-APACHE-2.0 +201 -0
- NOTICE +19 -0
- README.md +193 -0
- amp-tadpole.onnx +3 -0
- config.json +21 -0
- instructions.json +0 -0
- model.py +135 -0
- model.yaml +15 -0
- scene.png +0 -0
- weights.safetensors +3 -0
LICENSE
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| 407 |
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Creative Commons may be contacted at creativecommons.org.
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| 408 |
+
|
LICENSE-APACHE-2.0
ADDED
|
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|
| 1 |
+
Apache License
|
| 2 |
+
Version 2.0, January 2004
|
| 3 |
+
http://www.apache.org/licenses/
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+
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| 5 |
+
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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"Legal Entity" shall mean the union of the acting entity and all
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| 21 |
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"You" (or "Your") shall mean an individual or Legal Entity
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exercising permissions granted by this License.
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| 28 |
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not limited to compiled object code, generated documentation,
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NOTICE
ADDED
|
@@ -0,0 +1,19 @@
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|
| 1 |
+
AMP Tadpole
|
| 2 |
+
Copyright 2026 Adaptive Machine Patterns Ltd
|
| 3 |
+
(registered in England & Wales, no. 15618869)
|
| 4 |
+
|
| 5 |
+
The model weights (weights.safetensors, amp-tadpole.onnx), model.py,
|
| 6 |
+
config.json and model.yaml are licensed under the Creative Commons
|
| 7 |
+
Attribution-NonCommercial 4.0 International licence (see LICENSE).
|
| 8 |
+
Commercial licences are available from Adaptive Machine Patterns Ltd:
|
| 9 |
+
amp@adaptive-machine-patterns.com,
|
| 10 |
+
https://adaptive-machine-patterns.com/amp-embodied-ai-workbench
|
| 11 |
+
|
| 12 |
+
instructions.json lists token ids produced by the tokenizer of
|
| 13 |
+
SmolVLM2-500M-Video-Instruct (HuggingFaceTB), which is licensed under the
|
| 14 |
+
Apache License, Version 2.0 (see LICENSE-APACHE-2.0). The tokenizer itself
|
| 15 |
+
is not included. AMP Tadpole is not otherwise derived from SmolVLM2: its
|
| 16 |
+
weights were trained from scratch.
|
| 17 |
+
|
| 18 |
+
SmolVLM: Redefining small and efficient multimodal models.
|
| 19 |
+
A. Marafioti et al., 2025. arXiv:2504.05299.
|
README.md
ADDED
|
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|
| 1 |
+
---
|
| 2 |
+
license: cc-by-nc-4.0
|
| 3 |
+
library_name: pytorch
|
| 4 |
+
pipeline_tag: robotics
|
| 5 |
+
tags:
|
| 6 |
+
- robotics
|
| 7 |
+
- vision-language-action
|
| 8 |
+
- vla
|
| 9 |
+
- imitation-learning
|
| 10 |
+
- onnx
|
| 11 |
+
- amp-toy-robotics
|
| 12 |
+
---
|
| 13 |
+
|
| 14 |
+
# AMP Tadpole
|
| 15 |
+
|
| 16 |
+
**A small vision-language-action model that drives a robot arm from three
|
| 17 |
+
camera images and a plain-English instruction.** It has 6.5M parameters
|
| 18 |
+
and runs on a laptop CPU (and, through ONNX, in a web browser).
|
| 19 |
+
|
| 20 |
+

|
| 21 |
+
|
| 22 |
+
AMP Tadpole is trained from scratch on the
|
| 23 |
+
[AMP Toy Robotics Benchmark](https://adaptive-machine-patterns.com), and is
|
| 24 |
+
the first model of [Adaptive Machine Patterns](https://adaptive-machine-patterns.com).
|
| 25 |
+
|
| 26 |
+
> **v0.1.0 is a preview.** Training is ongoing; later versions will be
|
| 27 |
+
> published under new tags (see [Versions](#versions)).
|
| 28 |
+
|
| 29 |
+
## What it does
|
| 30 |
+
|
| 31 |
+
Every 8 steps the model is given:
|
| 32 |
+
|
| 33 |
+
- the **top, side and isometric camera images** (512 × 512 RGB each);
|
| 34 |
+
- the **arm's state**: 6 joint angles and the gripper opening;
|
| 35 |
+
- **one instruction clause**, e.g. *"Pick up the lime star"*.
|
| 36 |
+
|
| 37 |
+
It returns **the next 8 steps** of joint angles and gripper opening. It is
|
| 38 |
+
never told where the objects are: it has to find them in the pictures and
|
| 39 |
+
work out which one the words mean. The benchmark's closed loop executes
|
| 40 |
+
the steps and asks again, until the model asks for no further motion.
|
| 41 |
+
|
| 42 |
+
Instructions: `pick up the X`, `move to the X`,
|
| 43 |
+
`move to the midpoint between X and Y`, `open the gripper`,
|
| 44 |
+
`close the gripper`, `return home`, chained with "then".
|
| 45 |
+
|
| 46 |
+
## Results
|
| 47 |
+
|
| 48 |
+
On the benchmark's standard 300 held-out scenes (never seen in training,
|
| 49 |
+
and not used to choose the checkpoint), with the model driving the arm
|
| 50 |
+
itself:
|
| 51 |
+
|
| 52 |
+
| clauses | pick | move | midpoint | open | close | home | all | path vs teacher |
|
| 53 |
+
|---|---|---|---|---|---|---|---|---|
|
| 54 |
+
| 898 | 83% | 90% | 63% | 100% | 100% | 99% | **88.2%** | 1.02 |
|
| 55 |
+
|
| 56 |
+
The reference teacher, a scripted controller that knows where everything
|
| 57 |
+
is, scores 99.4% on the same scenes.
|
| 58 |
+
|
| 59 |
+
Grounding tests (100 further held-out scenes; see the benchmark's README):
|
| 60 |
+
|
| 61 |
+
| named object | other objects | image swap | instruction swap | blank instruction |
|
| 62 |
+
|---|---|---|---|---|
|
| 63 |
+
| 0.96 | 0.31 | 1.00 | 1.00 | 0.92 |
|
| 64 |
+
|
| 65 |
+
Told "move to the X", its motion points the way the teacher's does
|
| 66 |
+
(0.96, where 1 is the same direction), and not toward the other objects
|
| 67 |
+
in the scene (0.31). When two objects swap places, or the instruction
|
| 68 |
+
names another object, its motion changes as much as the teacher's does
|
| 69 |
+
(1.00). So it uses both the cameras and the words.
|
| 70 |
+
|
| 71 |
+
**These numbers are a line in the sand, not a ceiling.** v0.1.0 was
|
| 72 |
+
stopped part-way through training, and further training is expected to
|
| 73 |
+
improve them. They are published as a reference point for small models
|
| 74 |
+
on the benchmark: something to beat.
|
| 75 |
+
|
| 76 |
+
These come from `amp-bench eval --policy amp-tadpole` on a CPU (about 45
|
| 77 |
+
minutes). Other hardware can differ in the last digits of the arithmetic,
|
| 78 |
+
so a clause or two may come out differently.
|
| 79 |
+
|
| 80 |
+
## Use it
|
| 81 |
+
|
| 82 |
+
With the benchmark (`pip install "amp-toy-robotics[packages]"`):
|
| 83 |
+
|
| 84 |
+
```bash
|
| 85 |
+
hf download Adaptive-Machine-Patterns/amp-tadpole --revision v0.1.0 --local-dir amp-tadpole
|
| 86 |
+
amp-bench eval --policy amp-tadpole
|
| 87 |
+
```
|
| 88 |
+
|
| 89 |
+
In Python, this repository is a model package:
|
| 90 |
+
|
| 91 |
+
```python
|
| 92 |
+
import importlib.util
|
| 93 |
+
from safetensors.torch import load_file
|
| 94 |
+
|
| 95 |
+
spec = importlib.util.spec_from_file_location("tadpole", "amp-tadpole/model.py")
|
| 96 |
+
tadpole = importlib.util.module_from_spec(spec); spec.loader.exec_module(tadpole)
|
| 97 |
+
model = tadpole.build()
|
| 98 |
+
model.load_state_dict(load_file("amp-tadpole/weights.safetensors"))
|
| 99 |
+
model.eval()
|
| 100 |
+
|
| 101 |
+
chunk = tadpole.predict(model, images, state, "Pick up the lime star") # (8, 7)
|
| 102 |
+
```
|
| 103 |
+
|
| 104 |
+
With ONNX Runtime (any language, or the browser): `amp-tadpole.onnx` takes
|
| 105 |
+
`images` uint8 (1, 3, 3, 512, 512), `tokens` int64 (1, 48), `token_mask`
|
| 106 |
+
bool (1, 48) and `state` float32 (1, 7), and returns `chunk` (1, 8, 7).
|
| 107 |
+
`instructions.json` gives the tokens for every canonical clause.
|
| 108 |
+
|
| 109 |
+
## How it works
|
| 110 |
+
|
| 111 |
+
| part | what it does |
|
| 112 |
+
|---|---|
|
| 113 |
+
| CNN, shared by the cameras | 512 × 512 pixels (plus two pixel-coordinate channels) to a 16 × 16 grid of tokens per camera |
|
| 114 |
+
| word embeddings | the clause's tokens (up to 48) |
|
| 115 |
+
| state token | the 7 numbers, with sines and cosines of the joint angles |
|
| 116 |
+
| 8 action tokens | learned; one per output step |
|
| 117 |
+
| transformer | 6 layers, width 256, 8 heads, over all 825 tokens |
|
| 118 |
+
| head | each action token gives one step's change; the changes are added up |
|
| 119 |
+
|
| 120 |
+
It predicts the 8 steps directly in one pass (no sampling), which suits a
|
| 121 |
+
teacher whose action is a fixed function of the state.
|
| 122 |
+
|
| 123 |
+
## Training
|
| 124 |
+
|
| 125 |
+
- **Data:** generated on the fly from scene seeds by the benchmark
|
| 126 |
+
(`amp_toy_robotics.data`), labelled by the scripted teacher. No external
|
| 127 |
+
data, no dataset files, no human demonstrations. Scenes from seed
|
| 128 |
+
9,000,000 up were never used.
|
| 129 |
+
- **Inputs only:** the images, the state and the text, under the
|
| 130 |
+
benchmark's rules. No object positions, no progress reward, no path
|
| 131 |
+
penalty.
|
| 132 |
+
- **Recipe:** 250,000 steps, batch 64, AdamW (peak learning rate 3e-4, 1,000-step warm-up, cosine decay; the run was extended and resumed several times), the
|
| 133 |
+
state hidden from half the samples (so the model must use the cameras),
|
| 134 |
+
and an auxiliary loss in which each image token redraws its patch of
|
| 135 |
+
the image. The redraw head is used only in training and is not included.
|
| 136 |
+
- **The word table** was cut to the 32 tokens the instructions use. This
|
| 137 |
+
changes no output, and takes the model from 19.2M to 6.5M parameters.
|
| 138 |
+
|
| 139 |
+
## Limitations
|
| 140 |
+
|
| 141 |
+
- It knows only this simulated world: one arm, three fixed cameras, 8
|
| 142 |
+
colours and 5 shapes. It has not seen a real camera.
|
| 143 |
+
- It reads only canonical clauses (`instructions.json`); the benchmark
|
| 144 |
+
rewrites free text into canonical form first.
|
| 145 |
+
- The midpoint instruction is its weakest (see Results).
|
| 146 |
+
- v0.1.0 is a preview, stopped before the end of its planned schedule.
|
| 147 |
+
|
| 148 |
+
## Versions
|
| 149 |
+
|
| 150 |
+
| version | tag | what changed |
|
| 151 |
+
|---|---|---|
|
| 152 |
+
| 0.1.0 | `v0.1.0` | first preview: step 250,000 |
|
| 153 |
+
|
| 154 |
+
Pin a version with `revision="v0.1.0"` (or `--revision v0.1.0`).
|
| 155 |
+
|
| 156 |
+
## Licence
|
| 157 |
+
|
| 158 |
+
**Free for non-commercial use** under
|
| 159 |
+
[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (see
|
| 160 |
+
LICENSE and NOTICE). **For commercial use**, licences are available from
|
| 161 |
+
Adaptive Machine Patterns Ltd:
|
| 162 |
+
[adaptive-machine-patterns.com/amp-embodied-ai-workbench](https://adaptive-machine-patterns.com/amp-embodied-ai-workbench)
|
| 163 |
+
or amp@adaptive-machine-patterns.com.
|
| 164 |
+
|
| 165 |
+
**No warranty.** AMP Tadpole is a research preview, provided "as is",
|
| 166 |
+
without warranty of any kind, and Adaptive Machine Patterns Ltd accepts
|
| 167 |
+
no liability for its use, as set out in sections 5 and 6 of the licence.
|
| 168 |
+
It is not suitable for controlling real machinery, or for any use where
|
| 169 |
+
a wrong movement could cause harm or damage.
|
| 170 |
+
|
| 171 |
+
## Credits
|
| 172 |
+
|
| 173 |
+
- The token ids in `instructions.json` come from the tokenizer of
|
| 174 |
+
[SmolVLM2-500M-Video-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct)
|
| 175 |
+
(Apache-2.0; Marafioti et al., *SmolVLM: Redefining small and efficient
|
| 176 |
+
multimodal models*, arXiv:2504.05299). The model's weights are AMP's own,
|
| 177 |
+
trained from scratch.
|
| 178 |
+
- Written with the help of AI coding assistants (Anthropic Claude, Qwen, OpenAI ChatGPT).
|
| 179 |
+
|
| 180 |
+
## Cite
|
| 181 |
+
|
| 182 |
+
```bibtex
|
| 183 |
+
@misc{amp_tadpole_2026,
|
| 184 |
+
author = {Whyte, Andy and {Adaptive Machine Patterns Ltd}},
|
| 185 |
+
title = {{AMP Tadpole}: a small vision-language-action model},
|
| 186 |
+
year = {2026},
|
| 187 |
+
version = {0.1.0},
|
| 188 |
+
publisher = {Hugging Face},
|
| 189 |
+
url = {https://huggingface.co/Adaptive-Machine-Patterns/amp-tadpole}
|
| 190 |
+
}
|
| 191 |
+
```
|
| 192 |
+
|
| 193 |
+
© 2026 Adaptive Machine Patterns Ltd, registered in England & Wales, no. 15618869.
|
amp-tadpole.onnx
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|
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:04af48ae3dfe4081933f690d2e4a89038bb33c4f62da5a76a57a1dc433117aff
|
| 3 |
+
size 28215128
|
config.json
ADDED
|
@@ -0,0 +1,21 @@
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|
|
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|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model": "amp-tadpole",
|
| 3 |
+
"version": "0.1.0",
|
| 4 |
+
"parameters": 6507335,
|
| 5 |
+
"inputs": {
|
| 6 |
+
"images": "uint8 (3, 3, 512, 512): top, side, iso",
|
| 7 |
+
"state": "(7,): 6 joint angles (rad) + gripper (0 open .. 1 closed)",
|
| 8 |
+
"clause": "one canonical instruction clause"
|
| 9 |
+
},
|
| 10 |
+
"output": "(8, 7): the next 8 joint states + gripper",
|
| 11 |
+
"training": {
|
| 12 |
+
"steps": 250000,
|
| 13 |
+
"batch_size": 64,
|
| 14 |
+
"learning_rate": 0.0003,
|
| 15 |
+
"state_dropout": 0.5,
|
| 16 |
+
"seeds": "from 0 (scenes from 9,000,000 up never used)",
|
| 17 |
+
"progress_reward": 0.0,
|
| 18 |
+
"path_weight": 0.0,
|
| 19 |
+
"success_on_100_held_out_scenes_during_training": 0.8629
|
| 20 |
+
}
|
| 21 |
+
}
|
instructions.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
model.py
ADDED
|
@@ -0,0 +1,135 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
| 1 |
+
# SPDX-FileCopyrightText: 2026 Adaptive Machine Patterns Ltd
|
| 2 |
+
# SPDX-License-Identifier: CC-BY-NC-4.0
|
| 3 |
+
"""AMP Tadpole: a small vision-language-action model for the AMP Toy
|
| 4 |
+
Robotics Benchmark.
|
| 5 |
+
|
| 6 |
+
in images uint8 (3, 3, 512, 512): the top, side and iso cameras
|
| 7 |
+
state (7,): 6 joint angles (rad) + gripper aperture (0 open, 1 closed)
|
| 8 |
+
clause one instruction clause, in canonical wording
|
| 9 |
+
out (8, 7): the next 8 joint states + gripper aperture
|
| 10 |
+
|
| 11 |
+
images -> a CNN shared by the cameras -> 16 x 16 tokens per camera
|
| 12 |
+
clause -> token ids (instructions.json) -> word embeddings
|
| 13 |
+
state -> one token
|
| 14 |
+
all tokens + 8 learned action tokens -> 6-layer transformer
|
| 15 |
+
each action token -> one step's change; the changes are added up
|
| 16 |
+
|
| 17 |
+
This file is a model package (see the benchmark's README): build() makes
|
| 18 |
+
the module, predict() runs it on one observation. It needs only PyTorch.
|
| 19 |
+
"""
|
| 20 |
+
|
| 21 |
+
from __future__ import annotations
|
| 22 |
+
|
| 23 |
+
import json
|
| 24 |
+
import math
|
| 25 |
+
from pathlib import Path
|
| 26 |
+
|
| 27 |
+
import numpy as np
|
| 28 |
+
import torch
|
| 29 |
+
import torch.nn as nn
|
| 30 |
+
|
| 31 |
+
HERE = Path(__file__).resolve().parent
|
| 32 |
+
GRID = 16 # tokens per camera side
|
| 33 |
+
CHUNK = 8 # steps per answer
|
| 34 |
+
STATE_DIM = 7
|
| 35 |
+
TOKEN_LEN = 48 # instruction slots
|
| 36 |
+
STATE_FEATURES = 31
|
| 37 |
+
INC_SCALE = [0.04] * 6 + [0.125] # one step's change, per output unit
|
| 38 |
+
JOINT_LIMITS = [(0.1, 0.8 * math.pi), (math.pi / 2, 3 * math.pi / 2),
|
| 39 |
+
(-0.7 * math.pi, 0.7 * math.pi), (-0.6 * math.pi, 0.6 * math.pi),
|
| 40 |
+
(-math.pi, math.pi), (-math.pi, math.pi)]
|
| 41 |
+
|
| 42 |
+
|
| 43 |
+
class Tadpole(nn.Module):
|
| 44 |
+
def __init__(self, vocab: int, d: int = 256, layers: int = 6, heads: int = 8):
|
| 45 |
+
super().__init__()
|
| 46 |
+
|
| 47 |
+
def block(i, o):
|
| 48 |
+
return nn.Sequential(
|
| 49 |
+
nn.Conv2d(i, o, 3, 2, 1), nn.GroupNorm(8, o), nn.GELU(),
|
| 50 |
+
nn.Conv2d(o, o, 3, 1, 1), nn.GroupNorm(8, o), nn.GELU())
|
| 51 |
+
self.cnn = nn.ModuleList([ # 512 -> 16
|
| 52 |
+
nn.Sequential(nn.Conv2d(5, 48, 7, 4, 3), nn.GroupNorm(8, 48), nn.GELU()),
|
| 53 |
+
block(48, 96), block(96, 192), block(192, d)])
|
| 54 |
+
self.pos = nn.Parameter(torch.zeros(1, GRID * GRID, d))
|
| 55 |
+
self.view = nn.Parameter(torch.zeros(3, 1, d))
|
| 56 |
+
self.word = nn.Embedding(vocab, d)
|
| 57 |
+
self.word_pos = nn.Parameter(torch.zeros(1, TOKEN_LEN, d))
|
| 58 |
+
self.state = nn.Linear(STATE_FEATURES, d)
|
| 59 |
+
self.query = nn.Parameter(torch.zeros(1, CHUNK, d))
|
| 60 |
+
layer = nn.TransformerEncoderLayer(d, heads, 4 * d, dropout=0.0,
|
| 61 |
+
batch_first=True, norm_first=True)
|
| 62 |
+
self.fuse = nn.TransformerEncoder(layer, layers, enable_nested_tensor=False)
|
| 63 |
+
self.head = nn.Sequential(nn.LayerNorm(d), nn.Linear(d, STATE_DIM))
|
| 64 |
+
self.register_buffer("lo", torch.tensor([a for a, _ in JOINT_LIMITS] + [0.0]), persistent=False)
|
| 65 |
+
self.register_buffer("hi", torch.tensor([b for _, b in JOINT_LIMITS] + [1.0]), persistent=False)
|
| 66 |
+
self.register_buffer("inc", torch.tensor(INC_SCALE), persistent=False)
|
| 67 |
+
|
| 68 |
+
def state_features(self, state):
|
| 69 |
+
"""The 7 numbers scaled to -1..1, plus sines and cosines of the joint
|
| 70 |
+
angles, so that small differences in pose are not lost."""
|
| 71 |
+
q = state[:, :6]
|
| 72 |
+
return torch.cat([2 * (state - self.lo) / (self.hi - self.lo) - 1,
|
| 73 |
+
torch.sin(2 * q), torch.cos(2 * q),
|
| 74 |
+
torch.sin(8 * q), torch.cos(8 * q)], dim=1)
|
| 75 |
+
|
| 76 |
+
def forward(self, images, tokens, token_mask, state):
|
| 77 |
+
"""images uint8 (B, 3, 3, H, W); tokens (B, 48) ids from
|
| 78 |
+
instructions.json; token_mask (B, 48) bool, True for real tokens;
|
| 79 |
+
state (B, 7). Returns (B, 8, 7): the next 8 states."""
|
| 80 |
+
B, H = images.shape[0], images.shape[-1]
|
| 81 |
+
x = images.float() / 255.0
|
| 82 |
+
w = self.word(tokens) + self.word_pos
|
| 83 |
+
ax = torch.linspace(-1, 1, H, device=images.device)
|
| 84 |
+
yy, xx = torch.meshgrid(ax, ax, indexing="ij")
|
| 85 |
+
f = torch.cat([x.flatten(0, 1) * 2 - 1,
|
| 86 |
+
xx.expand(B * 3, 1, H, H), yy.expand(B * 3, 1, H, H)], dim=1)
|
| 87 |
+
for stage in self.cnn:
|
| 88 |
+
f = stage(f)
|
| 89 |
+
f = f.flatten(2).transpose(1, 2) + self.pos
|
| 90 |
+
f = (f.view(B, 3, GRID * GRID, -1) + self.view).flatten(1, 2)
|
| 91 |
+
s = self.state(self.state_features(state))[:, None]
|
| 92 |
+
q = self.query.expand(B, -1, -1)
|
| 93 |
+
h = torch.cat([f, w, s, q], dim=1)
|
| 94 |
+
keep = torch.ones(B, f.shape[1], dtype=torch.bool, device=h.device)
|
| 95 |
+
tail = torch.ones(B, 1 + CHUNK, dtype=torch.bool, device=h.device)
|
| 96 |
+
h = self.fuse(h, src_key_padding_mask=~torch.cat([keep, token_mask, tail], dim=1))
|
| 97 |
+
steps = self.head(h[:, -CHUNK:]) * self.inc
|
| 98 |
+
return steps.cumsum(dim=1) + state[:, None]
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
_TABLE = None
|
| 102 |
+
|
| 103 |
+
|
| 104 |
+
def instructions() -> dict:
|
| 105 |
+
"""The token table: every canonical clause -> its token ids."""
|
| 106 |
+
global _TABLE
|
| 107 |
+
if _TABLE is None:
|
| 108 |
+
_TABLE = json.loads((HERE / "instructions.json").read_text())
|
| 109 |
+
return _TABLE
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
def encode(clause: str):
|
| 113 |
+
"""(ids, mask) for one canonical clause, each of length 48."""
|
| 114 |
+
t = instructions()
|
| 115 |
+
try:
|
| 116 |
+
ids = t["instructions"][clause]
|
| 117 |
+
except KeyError:
|
| 118 |
+
raise ValueError(f"AMP Tadpole only reads canonical clauses "
|
| 119 |
+
f"(e.g. 'Pick up the lime star'); got {clause!r}") from None
|
| 120 |
+
pad = t["pad"]
|
| 121 |
+
return ids + [pad] * (TOKEN_LEN - len(ids)), [True] * len(ids) + [False] * (TOKEN_LEN - len(ids))
|
| 122 |
+
|
| 123 |
+
|
| 124 |
+
def build() -> Tadpole:
|
| 125 |
+
return Tadpole(vocab=len(instructions()["vocab"]))
|
| 126 |
+
|
| 127 |
+
|
| 128 |
+
@torch.no_grad()
|
| 129 |
+
def predict(module: Tadpole, images, state, clause: str) -> np.ndarray:
|
| 130 |
+
dev = next(module.parameters()).device
|
| 131 |
+
ids, mask = encode(clause)
|
| 132 |
+
out = module(torch.as_tensor(np.asarray(images)[None], device=dev),
|
| 133 |
+
torch.tensor([ids], device=dev), torch.tensor([mask], device=dev),
|
| 134 |
+
torch.as_tensor(np.asarray(state, dtype=np.float32)[None], device=dev))
|
| 135 |
+
return out[0].cpu().numpy().astype(np.float64)
|
model.yaml
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# AMP Tadpole: a model package for the AMP Toy Robotics Benchmark
|
| 2 |
+
# (and the AMP Embodied AI Workbench). Format: the benchmark's README.
|
| 3 |
+
name: AMP Tadpole
|
| 4 |
+
version: 0.1.0
|
| 5 |
+
author: Andy Whyte, Adaptive Machine Patterns Ltd
|
| 6 |
+
license: CC-BY-NC-4.0 (commercial licences from Adaptive Machine Patterns Ltd)
|
| 7 |
+
homepage: https://huggingface.co/Adaptive-Machine-Patterns/amp-tadpole
|
| 8 |
+
# The model's tokens in order, as (group, count).
|
| 9 |
+
token_map:
|
| 10 |
+
- [top camera, 256]
|
| 11 |
+
- [side camera, 256]
|
| 12 |
+
- [iso camera, 256]
|
| 13 |
+
- [instruction, 48]
|
| 14 |
+
- [state, 1]
|
| 15 |
+
- [action slots, 8]
|
scene.png
ADDED
|
weights.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:febca96f678fcb83292d07e243f6a8d392ceed74c854a9ea4477e8093bab5e0e
|
| 3 |
+
size 26039652
|