Action Chunking with Transformers (ACT) 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. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…
Open-weight model · Robotics
act-stress-ball-5000
by Oluwatomisin Fasawe tomisinf/act-stress-ball-5000
act-stress-ball-5000 is an open-weight model for robotics from Oluwatomisin Fasawe, released under Apache License 2.0. It has 52M parameters. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Action Chunking with Transformers (ACT) 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.
Parameters52M
Context—
Weights206.7 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve act-stress-ball-5000 (52M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
act-stress-ball-5000 on every accelerator the SAVRN Index prices, at every precision
Model Card
By Oluwatomisin Fasawe, published under apache-2.0, revision e0d1dd094186.
Action Chunking with Transformers (ACT) 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. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…
Read Oluwatomisin Fasawe's full model card
Model Card for act
Action Chunking with Transformers (ACT) 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.
This policy has been trained and pushed to the Hub using LeRobot.
Learn how to train and run it in the LeRobot act guide, or browse the full documentation.
Model Details
- License: apache-2.0
- Robot type:
so_follower - Cameras:
wrist,overhead
Inputs & Outputs
The policy consumes these observation features and produces these action features.
Inputs
| Feature | Type | Shape |
|---|---|---|
observation.state |
STATE | (6,) |
observation.images.wrist |
VISUAL | (3, 720, 1280) |
observation.images.overhead |
VISUAL | (3, 720, 1280) |
Outputs
| Feature | Type | Shape |
|---|---|---|
action |
ACTION | (6,) |
Training Dataset
- Repository: tomisinf/stress-ball-bowl-act-data
- Episodes: 62
- Frames: 44432
- Frame rate: 30 FPS
- Task(s): "Put the stress ball in the bowl"
Training Configuration
| Setting | Value |
|---|---|
| Training steps | 5000 |
| Batch size | 4 |
| Optimizer | adamw |
| Learning rate | 1e-05 |
| Seed | 1000 |
| LeRobot version | 0.6.1 |
How to Get Started with the Model
New to LeRobot? These guides cover the full workflow:
- Install LeRobot — set up the
lerobotpackage. - Hardware setup — assemble, wire, and calibrate your robot and cameras.
- Record data & train a policy — the end-to-end imitation-learning walkthrough.
- CLI cheat-sheet — quick reference for the
lerobot-*commands.
The short version to run and train this policy:
Run the policy on your robot
lerobot-rollout \
--strategy.type=base \
--robot.type=so_follower \
--robot.port=<your_robot_port> \
--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}}" \
--policy.path=tomisinf/act-stress-ball-5000 \
--task="Put the stress ball in the bowl" \
--duration=60
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.
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.
Train your own policy
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<policy_repo_id> \
--wandb.enable=true
Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.
Evaluation
No evaluation results have been provided for this policy yet.
Citation
If you use this policy, please cite the method linked in the description above, along with LeRobot:
@misc{cadene2024lerobot,
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},
title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
howpublished = "\url{https://github.com/huggingface/lerobot}",
year = {2024}
}
Identity and Version
- Repository
- tomisinf/act-stress-ball-5000
- Publisher
- Oluwatomisin Fasawe
- Task
- Robotics
- Modality
- Control
- Library
- lerobot
- Parameters
- 52M parameters
- Languages
- act
- Revision
- e0d1dd09418635f0b30ad305dda264bb308cb2fe
- First published
- 2026-10-16
- Last updated
- 2026-10-16
Files and Weights
9 files, 206.7 MB in total. The weights are 3 files totalling 206.7 MB in safetensors.
Weights3 files · 206.7 MB
Configuration4 files · 9.9 KB
Documentation1 file · 5.7 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 206.7 MB | cdd7e4f292ad |
| policy_postprocessor_step_0_unnormalizer_processor.safetensors | Weights | 7.6 KB | 494138847698 |
| policy_preprocessor_step_3_normalizer_processor.safetensors | Weights | 7.6 KB | 75c10d193150 |
| config.json | Configuration | 1.7 KB | — |
| policy_postprocessor.json | Configuration | 660 B | — |
| policy_preprocessor.json | Configuration | 1.3 KB | — |
| train_config.json | Configuration | 6.2 KB | — |
| README.md | Documentation | 5.7 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 206.7 MB
Download from Oluwatomisin Fasawe
Released by Oluwatomisin Fasawe through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2304.13705
- Trained on (disclosed) tomisinf/stress-ball-bowl-act-data
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 206.7 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About act-stress-ball-5000
How much GPU memory does act-stress-ball-5000 need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (52M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run act-stress-ball-5000 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use act-stress-ball-5000 commercially?
Yes. act-stress-ball-5000 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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