---
license: apache-2.0
task_categories:
- robotics
tags:
- robotics
- agent
- embodied-ai
- manipulation
size_categories:
- 10K<n<100K
---

# Show-Harness Data

The demonstrations behind [Show-Harness VLMs](https://huggingface.co/showlab/Show-Harness-VLMs):
two real embodiments (7-DoF Franka, 6-DoF AgileX) and two simulators (RoboLab, ManiSkill). One
observation, one action unit — every unit a 2 cm translation on every rig, so the subsets mix
without rescaling.

[Paper](https://arxiv.org/abs/2609.10522) ·
[Code](https://github.com/showlab/Show-Harness) ·
[Models](https://huggingface.co/showlab/Show-Harness-VLMs) ·
[Project page](https://showlab.github.io/Show-Harness/)

| split | contents | episodes | samples | images | size |
| --- | --- | :-: | :-: | :-: | :-: |
| [`real/`](real) | Franka (101) + AgileX (63), 17 tasks | 164 | 7,933 | 15,866 | 865 MB |
| [`sim/`](sim) | RoboLab (130) + ManiSkill (100) | 230 | 13,753 | 27,046 | 1.5 GB |

Each split is subdivided by fields in `episodes.jsonl`, not by directory. Within `sim/`,
`rollout_000..129` are RoboLab and `rollout_130..229` are ManiSkill — but read the `simulator`
field rather than the numbering, which is an implementation detail.

## Layout

```
Show-Harness-Data/
├── real/
│   ├── rollouts.json          training samples
│   ├── episodes.jsonl         one line per episode
│   └── metadata/rollout_000/  actions.jsonl + agentview/*.png + wrist/*.png
└── sim/                       same three pieces
```

**`rollouts.json`** — Alpaca multimodal format, one sample per action step. Image paths are
relative to the file itself, always agentview first and wrist second, matching the two leading
`<image>` markers. Nine units: six `MV_*` directions (`FWD` `BACK` `LEFT` `RIGHT` `UP` `DOWN`),
`GRASP`, `RELEASE`, and the terminal `DONE`.

```json
{
  "instruction": "<image><image>" + rendered prompt,
  "input": "",
  "output": "MV_DOWN",
  "images": ["metadata/rollout_000/agentview/0000.png",
             "metadata/rollout_000/wrist/0000.png"]
}
```

**`episodes.jsonl`** — one line per episode, indexing by embodiment, task and environment.
`embodiment` is `franka` (overhead exocentric) or `piper` (AgileX, first-person). Simulation
episodes also carry `simulator`, `env_id` and the `seed` that reproduces them; ManiSkill episodes
add `shard` and `scheme`.

**`actions.jsonl`** — the per-episode capture log (action, gripper state, end-effector pose, frame
names), useful for deriving alternative supervision targets such as continuous EE deltas. It is
the raw capture, not the supervision target: **train on `rollouts.json`**.

## Image transform

Both views go through one transform — rotate/flip, centre-crop to 4:3, letterbox into 256×256 — so
a wrist frame carries 32 black rows top and bottom on every subset, real and simulated alike. A
policy trained on one source sees the same framing on any other.

## ⚠️ Direction convention on the AgileX arm

Directional units follow the Franka rig's overhead exocentric view throughout, a single convention
shared by both embodiments. The AgileX rig observes from a first-person view that mirrors the
forward–backward axis, so **deployment to an AgileX arm requires exchanging `MV_FWD` and
`MV_BACK`** — otherwise the arm moves the wrong way, with no error raised. Franka and simulation
require no conversion.

## Usage

To train with the pipeline in the [Show-Harness repository](https://github.com/showlab/Show-Harness):

```bash
git clone https://github.com/showlab/Show-Harness && cd Show-Harness

bash train/scripts/download_dataset.sh            # fetch + register both splits
bash train/scripts/setup_llamafactory.sh          # one-time: clone upstream, build the venv

cp train/configs/qwen3_5_2b_lora.yaml train/configs/my_run.yaml   # set dataset: showharness_sim
CONFIG=train/configs/my_run.yaml GPU=0,1 bash train/scripts/train.sh
```

`media_dir` resolves automatically from the registered path, so the relative image paths work with
no further configuration.

## Models

The adapters trained on these splits are at
[Show-Harness-VLMs](https://huggingface.co/showlab/Show-Harness-VLMs) — five real-robot policies
from `real/` (40 epochs, one per backbone) and `qwen3_5_2b_sim` from `sim/` (30 epochs, one policy
for both simulators).

## Citation

```bibtex
@misc{chen2026showharnessjustvlmagent,
      title={Show-Harness: Just a VLM Agent Can Play Robots}, 
      author={Yanzhe Chen and Zechen Bai and Zhijun Cao and Wenzheng Zeng and Kevin Qinghong Lin and Yiqi Lin and Guoqiang Liang and Kevin Yuchen Ma and Qiming Huang and Mike Zheng Shou},
      year={2026},
      eprint={2609.10522},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2609.10522}, 
}
```