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library_name: lerobot
license: mit
pipeline_tag: robotics
datasets:
- lerobot/aloha_sim_transfer_cube_human
tags:
- robotics
- lerobot
- act
- imitation-learning
- aloha
- simulation
model-index:
- name: act_aloha_sim_transfer_cube
results:
- task:
type: robotics
name: AlohaTransferCube-v0 (gym-aloha)
dataset:
type: lerobot/aloha_sim_transfer_cube_human
name: aloha_sim_transfer_cube_human
metrics:
- type: success_rate
value: 75.5
name: success rate over 200 episodes (95% Wilson CI 69.1-80.9)
---
# ACT on ALOHA TransferCube, trained from scratch on an Apple M4 laptop
An [ACT](https://arxiv.org/abs/2304.13705) policy for the `AlohaTransferCube-v0` simulation task,
trained with [LeRobot](https://github.com/huggingface/lerobot) 0.7.0 on the
[`lerobot/aloha_sim_transfer_cube_human`](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human)
dataset (50 human demonstrations). Training ran on an Apple M4 MacBook Pro (MPS, no NVIDIA GPU) for
80k steps at batch size 8, about 10 hours in total. LeRobot's default ACT hyperparameters, nothing tuned.
Code, evaluation scripts, per-checkpoint results and training curves:
[github.com/ChadSaglam/lerobot-sim-transfer-cube](https://github.com/ChadSaglam/lerobot-sim-transfer-cube).
## Evaluation
Same simulator, same episode seeds (`--seed=1000`), 200 episodes, 95% Wilson intervals from `lerobot-eval`.
| Policy | Training steps | Success | 95% CI |
|---|---|---|---|
| official [`lerobot/act_aloha_sim_transfer_cube_human`](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human) | 80k | 81.0% (162/200) | 75.0-85.8 |
| this model (seed 1000) | 80k | 75.5% (151/200) | 69.1-80.9 |
| same run at 50k steps | 50k | 64.0% (128/200) | 57.1-70.3 |
| second seed (1001) at 50k steps | 50k | 62.0% (124/200) | 55.1-68.4 |
The intervals of this model and the official checkpoint overlap; the two are statistically
indistinguishable at this sample size. The 50k checkpoints are clearly below the baseline.
Two things the per-checkpoint evaluations showed (details in the GitHub README): the training loss did
not predict task success (two seeds with identical loss curves differed by 34 points in success at 10k
steps), and 50-episode evaluations cannot rank neighbouring checkpoints.
## How to use
```bash
pip install 'lerobot[aloha]==0.7.0'
lerobot-eval \
--policy.path=ChadSaglam/act_aloha_sim_transfer_cube \
--env.type=aloha \
--env.task=AlohaTransferCube-v0 \
--eval.n_episodes=200 \
--eval.batch_size=10 \
--seed=1000 \
--policy.device=cuda # mps on Apple Silicon; add --eval.use_async_envs=false on macOS
```
## Training
```bash
lerobot-train \
--policy.type=act \
--policy.device=mps \
--policy.push_to_hub=false \
--dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \
--env.type=aloha \
--env.task=AlohaTransferCube-v0 \
--env_eval_freq=0 \
--seed=1000 \
--steps=50000 \
--batch_size=8 \
--save_freq=10000 \
--output_dir=outputs/train/act_transfer_cube_seed1000
# then resumed from the 50k checkpoint with --resume=true --steps=80000
```
- Policy: ACT, ResNet-18 backbone (ImageNet init), chunk size 100, 100 action steps, VAE on, KL weight 10,
AdamW, learning rate 1e-5, batch size 8. `train_config.json` in this repository has every setting.
- Throughput on the M4: 2.2 steps/s. 50k steps in 6 h 22 min, the 30k-step extension in 3 h 47 min.
- Checkpoint format: LeRobot 0.7.0 processor pipeline (`policy_preprocessor.json`, `policy_postprocessor.json`).
## Limitations
- Simulation only; one task; one evaluation seed set. Another `--seed` would move every number by a few points.
- Success is the `gym-aloha` definition (cube lifted by the right arm and transferred to the left gripper).
- No hyperparameter tuning; LeRobot defaults throughout.
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