---
license: apache-2.0
library_name: lerobot
tags: [robotics, lerobot, act, tactile, manipulation]
datasets: [aryankakad/tactile_charger_inserting]
---

# act_vision_charger_50ep

**ACT** policy — **vision only** — trained on **50 episodes**.

Task: *grab and remove the charger from the socket and put it in the black box*

Trained with [LeFlexiTac](https://github.com/TNA001-AI/lerobot_tactile), a LeRobot fork adding
FlexiTac tactile sensing. Docs: <https://tna001-ai.github.io/LeFlexiTac/docs.html>

## Training data

| | |
|---|---|
| dataset | [`aryankakad/tactile_charger_inserting`](https://huggingface.co/datasets/aryankakad/tactile_charger_inserting) |
| episodes | 50 (all) |
| frames | 27,973 @ 30 fps |
| cameras | `observation.images.top`, `observation.images.gripper` (224×224) |
| tactile | **not used** — vision-only baseline |
| state / action | 6-DoF SO-100 follower |

## Configuration

| | |
|---|---|
| steps | 100,000 |
| batch size | 32 |
| **epochs** | **114.4** |
| `chunk_size` | `100` |
| `n_action_steps` | `100` |
| `vision_backbone` | `resnet18` |
| `dim_model` | `512` |
| `n_encoder_layers` | `4` |
| `n_decoder_layers` | `1` |
| `use_vae` | `True` |
| `kl_weight` | `10.0` |
| `optimizer_lr` | `1e-05` |
| `optimizer_weight_decay` | `0.0001` |

Every model in this series is **epoch-matched at ~114.4 epochs**, so dataset size and
sensor modality are the only variables across the set.

## Training command actually used

Run on 1× AMD Instinct MI300X (ROCm 6.2.4). `HIP_VISIBLE_DEVICES` selected the GPU,
so `--policy.device=cuda` refers to that single card.

```bash
python -u -m lerobot.scripts.lerobot_train \
  --dataset.repo_id=aryankakad/tactile_charger_inserting \
  --policy.type=act \
  --policy.repo_id=Dimios45/act_vision_charger_50ep \
  --policy.private=true --policy.device=cuda \
  --output_dir=outputs/train/A_act_vision_50ep --job_name=A_act_vision_50ep \
  --batch_size=32 --num_workers=8 --steps=100000 --save_freq=20000 --wandb.enable=true
```

## Evaluation / rollout

Not run here — this machine has no robot attached. To evaluate, run on the machine with the
SO-100 and sensors, loading the policy with `--policy.path=Dimios45/act_vision_charger_50ep`.

Reference: the `lerobot-record` eval invocations in
[`tactile_cmd.txt`](https://github.com/TNA001-AI/lerobot_tactile/blob/main/tactile_cmd.txt)
and the [project docs](https://tna001-ai.github.io/LeFlexiTac/docs.html). You will need to
supply your own robot port, camera serials.

## Notes

- Two ROCm-specific fixes were required in the fork: `persistent_workers=True` on the
  dataloader (epoch boundaries otherwise stalled ~410 s each), and keeping
  `cudnn.benchmark` **off** (on ROCm it triggers an exhaustive MIOpen search that can
  precede step 1 by hours).
- Training loss is **not** a proxy for task success. Compare policies by rollout success
  rate, especially on contact-rich phases.
- The source dataset's task string is labelled `stack cup` — a mislabel carried over from
  an earlier session. It does not affect ACT, which is not language-conditioned.