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_aloha_sim_transfer_cube_human
by LeRobot lerobot/act_aloha_sim_transfer_cube_human
Action Chunking Transformer Policy (as per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware) trained for the AlohaTransferCube environment from gym-aloha.
Parameters52M
Context—
Weights206.8 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads2.9k
Runs On
What it takes to serve act_aloha_sim_transfer_cube_human (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 Sep 18, 2026.
Model Card
By LeRobot, published under apache-2.0, revision ba73b2766f13.
Action Chunking Transformer Policy (as per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware) trained for the AlohaTransferCube environment from gym-aloha. See the LeRobot library (particularly the evaluation script) for instructions on how to load and evaluate this model. Trained with LeRobot@3c0a209. The model was trained using LeRobot's training script and with the alohasimtransfercubehuman dataset, using this command: The training curves may be found at https://wandb.ai/aliberts/lerobot/runs/720l37xb. The current model corresponds to the checkpoint at 80k steps. This took about 1h45 to train on an Nvida A100. The model was evaluated on the AlohaTransferCube task from…
Read LeRobot's full model card
Model Card for ACT/AlohaTransferCube
Action Chunking Transformer Policy (as per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware) trained for the AlohaTransferCube environment from gym-aloha.
How to Get Started with the Model
See the LeRobot library (particularly the evaluation script) for instructions on how to load and evaluate this model.
Training Details
Trained with LeRobot@3c0a209.
The model was trained using LeRobot's training script and with the aloha_sim_transfer_cube_human dataset, using this command:
python lerobot/scripts/train.py \
--output_dir=outputs/train/act_aloha_transfer \
--policy.type=act \
--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
--env.type=aloha \
--env.task=AlohaTransferCube-v0 \
--wandb.enable=true
The training curves may be found at https://wandb.ai/aliberts/lerobot/runs/720l37xb. The current model corresponds to the checkpoint at 80k steps.
This took about 1h45 to train on an Nvida A100.
Evaluation
The model was evaluated on the AlohaTransferCube task from gym-aloha and compared to a similar model trained with the original ACT repository. Each episode marks a success if the cube is successfully picked by one robot arm and transferred to the other robot arm.
Here are the success rate results for 500 episodes worth of evaluation. The "Theirs" column is for an equivalent model trained on the original ACT repository and evaluated on LeRobot (the model weights may be found in the original_act_repo branch of this respository). The results of each of the individual rollouts may be found in eval_info.json.
| Ours | Theirs | |
|---|---|---|
| Success rate for 500 episodes (%) | 83.0 | 68.0 |
It was produced after training with this command:
python lerobot/scripts/eval.py \
--policy.path=outputs/train/act_aloha_transfer/checkpoints/080000/pretrained_model \
--output_dir=outputs/eval/act_aloha_transfer/080000 \
--env.type=aloha \
--env.task=AlohaTransferCube-v0 \
--eval.n_episodes=500 \
--eval.batch_size=50 \
--device=cuda \
--use_amp=false
The original code was heavily refactored, and some bugs were spotted along the way. The differences in code may account for the difference in success rate. Another possibility is that our simulation environment may use slightly different heuristics to evaluate success (we've observed that success is registered as soon as the second arm's gripper makes antipodal contact with the cube). Finally, one should observe that the in-training evaluation jumps up towards the end of training. This may need further investigation (Is it statistically significant? If so, what is the cause?).
Identity and Version
- Repository
- lerobot/act_aloha_sim_transfer_cube_human
- Publisher
- LeRobot
- Task
- Robotics
- Modality
- Control
- Library
- transformers
- Parameters
- 52M parameters
- Languages
- act-policy
- Revision
- ba73b2766f1371cdc133ca4efb97eb090d744625
- First published
- 2024-05-05
- Last updated
- 2025-03-06
Files and Weights
8 files, 212.8 MB in total. The weights are 1 file totalling 206.8 MB in safetensors.
Weights1 file · 206.8 MB
Configuration3 files · 73.5 KB
Documentation1 file · 3.5 KB
Other2 files · 5.9 MB
Repository1 file · 1.6 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 206.8 MB | 42772891cb6e |
| config.json | Configuration | 1.3 KB | — |
| eval_info.json | Configuration | 66.8 KB | — |
| train_config.json | Configuration | 5.3 KB | — |
| README.md | Documentation | 3.5 KB | — |
| demo.gif | Other | 5.8 MB | 00f975b0fd48 |
| training_curves.png | Other | 76.6 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 206.8 MB
Download from LeRobot
Released by LeRobot through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2304.13705
- Trained on (disclosed) lerobot/aloha_sim_transfer_cube_human
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 206.8 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_aloha_sim_transfer_cube_human
How much GPU memory does act_aloha_sim_transfer_cube_human 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_aloha_sim_transfer_cube_human 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_aloha_sim_transfer_cube_human commercially?
Yes. act_aloha_sim_transfer_cube_human 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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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…
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…
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…
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. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.