Robotics
LeRobot
Safetensors
act
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Upload policy weights, train config and readme

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  1. README.md +126 -25
  2. config.json +42 -50
  3. model.safetensors +2 -2
  4. train_config.json +100 -74
README.md CHANGED
@@ -1,63 +1,164 @@
1
  ---
2
- base_model: lerobot/smolvla_base
3
- datasets: chabir78/bottles_and_cans_ws_1
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  library_name: lerobot
5
  license: apache-2.0
6
- model_name: smolvla
7
  pipeline_tag: robotics
8
  tags:
9
- - robotics
10
- - smolvla
11
  - lerobot
 
12
  ---
13
 
14
- # Model Card for smolvla
15
 
16
  <!-- Provide a quick summary of what the model is/does. -->
17
 
18
 
19
- [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
 
20
 
21
 
 
 
 
 
 
 
 
 
22
  This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
23
- See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
24
 
25
  ---
26
 
27
  ## How to Get Started with the Model
28
 
29
- For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
30
- Below is the short version on how to train and run inference/eval:
 
 
 
 
31
 
32
- ### Train from scratch
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
33
 
34
  ```bash
35
  lerobot-train \
36
  --dataset.repo_id=${HF_USER}/<dataset> \
37
  --policy.type=act \
38
- --output_dir=outputs/train/<desired_policy_repo_id> \
39
  --job_name=lerobot_training \
40
  --policy.device=cuda \
41
- --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
42
  --wandb.enable=true
43
  ```
44
 
45
- _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
46
 
47
- ### Evaluate the policy/run inference
48
 
49
- ```bash
50
- lerobot-record \
51
- --robot.type=so100_follower \
52
- --dataset.repo_id=<hf_user>/eval_<dataset> \
53
- --policy.path=<hf_user>/<desired_policy_repo_id> \
54
- --episodes=10
55
- ```
56
 
57
- Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
 
 
 
 
 
 
 
 
 
 
 
58
 
59
  ---
60
 
61
- ## Model Details
 
 
62
 
63
- - **License:** apache-2.0
 
 
 
 
 
 
 
 
1
  ---
2
+ datasets: chabir78/remove_cap_from_bottles_001
 
3
  library_name: lerobot
4
  license: apache-2.0
5
+ model_name: act
6
  pipeline_tag: robotics
7
  tags:
8
+ - act
 
9
  - lerobot
10
+ - robotics
11
  ---
12
 
13
+ # Model Card for act
14
 
15
  <!-- Provide a quick summary of what the model is/does. -->
16
 
17
 
18
+ [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
19
+
20
 
21
 
22
+
23
+ <!-- A short demo is worth more than any description! Record a GIF/video of the policy
24
+ running on your robot, upload it to this repo, and embed it here:
25
+ <p align="center">
26
+ <img src="https://huggingface.co/<hf_user>/<policy_repo_id>/resolve/main/demo.gif" width="60%"/>
27
+ </p>
28
+ -->
29
+
30
  This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
31
+
32
+ Learn how to train and run it in the [LeRobot act guide](https://huggingface.co/docs/lerobot/main/en/act), or browse the [full documentation](https://huggingface.co/docs/lerobot/index).
33
+
34
+
35
+ ---
36
+
37
+ ## Model Details
38
+
39
+ - **License:** apache-2.0
40
+ - **Robot type:** `bi_so_follower`
41
+ - **Cameras:** `left_wrist`, `left_top`, `right_wrist`
42
+
43
+
44
+ ## Inputs & Outputs
45
+
46
+ The policy consumes these observation features and produces these action features.
47
+
48
+ **Inputs**
49
+
50
+ | Feature | Type | Shape |
51
+ | --- | --- | --- |
52
+ | `observation.state` | STATE | `(12,)` |
53
+ | `observation.images.left_wrist` | VISUAL | `(3, 480, 640)` |
54
+ | `observation.images.left_top` | VISUAL | `(3, 480, 640)` |
55
+ | `observation.images.right_wrist` | VISUAL | `(3, 480, 640)` |
56
+
57
+ **Outputs**
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+
59
+ | Feature | Type | Shape |
60
+ | --- | --- | --- |
61
+ | `action` | ACTION | `(12,)` |
62
+
63
+
64
+ ## Training Dataset
65
+
66
+ - **Repository:** [chabir78/remove_cap_from_bottles_001](https://huggingface.co/datasets/chabir78/remove_cap_from_bottles_001)
67
+ - **Episodes:** 76
68
+ - **Frames:** 192912
69
+ - **Frame rate:** 30 FPS
70
+ - **Task(s):** "remove the cap from the bottle"
71
+
72
+ <a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=chabir78/remove_cap_from_bottles_001">
73
+ <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
74
+ <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/>
75
+ </a>
76
+
77
+
78
+ ## Training Configuration
79
+
80
+ | Setting | Value |
81
+ | --- | --- |
82
+ | Training steps | 100000 |
83
+ | Batch size | 8 |
84
+ | Optimizer | adamw |
85
+ | Learning rate | 1e-05 |
86
+ | Seed | 1000 |
87
+ | LeRobot version | 0.6.1 |
88
 
89
  ---
90
 
91
  ## How to Get Started with the Model
92
 
93
+ New to LeRobot? These guides cover the full workflow:
94
+
95
+ - **[Install LeRobot](https://huggingface.co/docs/lerobot/main/en/installation)** — set up the `lerobot` package.
96
+ - **[Hardware setup](https://huggingface.co/docs/lerobot/main/en/hardware_guide)** — assemble, wire, and calibrate your robot and cameras.
97
+ - **[Record data & train a policy](https://huggingface.co/docs/lerobot/en/il_robots)** — the end-to-end imitation-learning walkthrough.
98
+ - **[CLI cheat-sheet](https://huggingface.co/docs/lerobot/main/en/cheat-sheet)** — quick reference for the `lerobot-*` commands.
99
 
100
+ The short version to run and train this policy:
101
+
102
+ ### Run the policy on your robot
103
+
104
+ ```bash
105
+ lerobot-rollout \
106
+ --strategy.type=base \
107
+ --robot.type=bi_so_follower \
108
+ --robot.port=<your_robot_port> \
109
+ --robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
110
+ --policy.path=True \
111
+ --task="remove the cap from the bottle" \
112
+ --duration=60
113
+ ```
114
+
115
+ Replace the remaining `<...>` placeholders with your own values: `--robot.port` and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on.
116
+
117
+ When `--strategy.type=base` is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at [rollout documentation](https://huggingface.co/docs/lerobot/main/en/inference).
118
+
119
+ ### Train your own policy
120
 
121
  ```bash
122
  lerobot-train \
123
  --dataset.repo_id=${HF_USER}/<dataset> \
124
  --policy.type=act \
125
+ --output_dir=outputs/train/<policy_repo_id> \
126
  --job_name=lerobot_training \
127
  --policy.device=cuda \
128
+ --policy.repo_id=${HF_USER}/<policy_repo_id> \
129
  --wandb.enable=true
130
  ```
131
 
132
+ _Writes checkpoints to `outputs/train/<policy_repo_id>/checkpoints/`._
133
 
134
+ ---
135
 
136
+ ## Evaluation
 
 
 
 
 
 
137
 
138
+ <!-- Report real-robot results here: run the policy several times per task and count the
139
+ successes. Delete the "No evaluation results" line and fill in this table instead:
140
+
141
+ | Task | Trials | Successes | Success rate |
142
+ | ---- | ------ | --------- | ------------ |
143
+ | pick the lego brick | 10 | 8 | 80% |
144
+
145
+ Also worth noting: anything that affects difficulty (new object positions, lighting,
146
+ distractors, a different robot of the same type, ...).
147
+ -->
148
+
149
+ _No evaluation results have been provided for this policy yet._
150
 
151
  ---
152
 
153
+ ## Citation
154
+
155
+ If you use this policy, please cite the method linked in the description above, along with LeRobot:
156
 
157
+ ```bibtex
158
+ @misc{cadene2024lerobot,
159
+ author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
160
+ title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
161
+ howpublished = "\url{https://github.com/huggingface/lerobot}",
162
+ year = {2024}
163
+ }
164
+ ```
config.json CHANGED
@@ -1,19 +1,14 @@
1
  {
2
- "type": "smolvla",
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- "normalization_mapping": {
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