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OSCAR — public inference release

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# OSCAR: Omni-Embodiment Action-Conditioned World Model for Robotics

[Zhuoyuan Wu](https://wuzy2115.github.io)1 &nbsp;&nbsp; [Jun Gao](https://www.cs.toronto.edu/~jungao/)2,3

1 Peking University &nbsp;&nbsp; 2 University of Michigan &nbsp;&nbsp; 3 NVIDIA

</div>

> **Abstract.** We present OSCAR, a precise action-conditioned video world model that generalizes across different robot embodiments and enables robot policy evaluation. Existing video world models face three main challenges for real-world robot evaluation: limited scenario diversity in current robot training datasets, imprecise action following, and poor generalization across embodiments for broad adoption. We tackle these challenges from two perspectives. At its core is a large-scale standardized data pipeline that curates, filters, and deduplicates broad robotics and egocentric human datasets, yielding a clean joint-training dataset that spans diverse tasks, scenarios, actions, and robot embodiments. To condition the video model, we adopt 2D kinematic skeleton rendering as a unified conditioning representation that generalizes across different robot arms or even human hands. We finetune the Cosmos-Predict2.5-2B model on a single GH200 GPU. Our model achieves significant improvement on action following, appearance quality, and motion consistency, compared to existing baselines, which either have a much larger model size or require more GPUs. We further deploy OSCAR to evaluate robot policies from RoboArena. Extensive experiments demonstrate the significant correlation between our virtual policy evaluation in OSCAR and real-world evaluation, paving the way for the future where robot policies can be purely evaluated in virtual generated worlds.

## Gallery

Each tile shows *OSCAR output · skeleton conditioning · ground truth* for one embodiment.

<table>
 <tr>
 <td align="center" width="50%"><img src="docs/teaser/agibot.gif" width="100%"> AgiBot G1 · Bimanual</td>
 <td align="center" width="50%"><img src="docs/teaser/airoa.gif" width="100%"> AIROA HSR · Mobile Manipulation</td>
 </tr>
 <tr>
 <td align="center" width="50%"><img src="docs/teaser/droid.gif" width="100%"> DROID Franka · Tabletop</td>
 <td align="center" width="50%"><img src="docs/teaser/interndata.gif" width="100%"> InternData Franka · Pick &amp; Place</td>
 </tr>
 <tr>
 <td align="center" width="50%"><img src="docs/teaser/rh20t_cfg5.gif" width="100%"> RH20T Franka · Contact-Rich</td>
 <td align="center" width="50%"><img src="docs/teaser/rh20t_cfg7.gif" width="100%"> RH20T KUKA · Contact-Rich</td>
 </tr>
 <tr>
 <td align="center" width="50%"><img src="docs/teaser/egodex.gif" width="100%"> EgoDex · Human Egocentric</td>
 <td align="center" width="50%"><img src="docs/teaser/epic.gif" width="100%"> EPIC-Kitchens · Human Egocentric</td>
 </tr>
</table>

## 🫨 News

- **2026-06-16** — Added a self-contained [skeleton rendering demo](demo/): renders a DROID (Franka + Robotiq) episode to show how robot proprioception becomes the 2D kinematic-skeleton conditioning signal — read state → URDF FK → project → overlay, CPU-only.
- **2026-06-16** — Released the [OSCAR policy-rollout videos](https://huggingface.co/datasets/zywu2115/OSCAR_policy_rollout): real-robot rollouts paired with OSCAR world-model rollouts from the RoboArena policy-evaluation experiment.
- **2026-05-14** — Initial release: OSCAR-2B model weights, inference CLI, and `oscar_diffusers` / `oscar_diffsynth` wrapper libraries.

## Quick Start

### Prerequisites

- Linux x86_64 with an NVIDIA GPU (≥ 24 GB VRAM recommended; tested on
  RTX PRO 6000 Blackwell, sm_120).
- CUDA 12.4+ runtime drivers.
- `uv` (`curl -LsSf https://astral.sh/uv/install.sh | sh`).
- `git`, `ffmpeg` (system-level — `imageio[ffmpeg]` bundles a userspace
  fallback if you can't install ffmpeg system-wide).

<details>
<summary>Optional: TransformerEngine (source build, FP8/Blackwell-optimized attention)</summary>

The inference path runs without TransformerEngine; it falls back to PyTorch SDPA. If you want the FP8/optimized attention kernels, build TE from source:

```
git clone --recursive https://github.com/NVIDIA/TransformerEngine.git
cd TransformerEngine
NVTE_FRAMEWORK=pytorch TORCH_CUDA_ARCH_LIST="12.0" pip install -v .
```

TransformerEngine is not currently shipped as a stable pip wheel for the CUDA 12.8 / Blackwell stack, hence the source build. Adjust `TORCH_CUDA_ARCH_LIST` to match your GPU (`9.0` for Hopper, `8.9` for Ada, etc.). Skip this section if you are happy with SDPA attention — the public benchmark cases were generated without TE.

</details>

### 1. Install (uv)

```
git clone https://github.com/wuzy2115/oscar.git
cd oscar
uv venv --python 3.10 .venv
uv pip install -r requirements_minimal.txt
```

### 2. Download model + assets

```
hf download zywu2115/OSCAR-2B --local-dir checkpoints/
```

Pulls `checkpoints/model/` (~5 GB, distributed checkpoint shards) and
`checkpoints/assets/` (~500 MB, 14 benchmark cases) in one go.

### 3. Run inference

```
bash scripts/run_inference.sh agibot_465
```

Output: `outputs/agibot_465.mp4` (81 frames, 480×640, H.264, ~7 MB).
Wall time on a single Blackwell GPU is ≈ 1 minute after the first warmup
load.

### 4. Library wrappers (diffusers / DiffSynth)

Both wrappers require the same environment from steps 1–2 above (they delegate to `inference._core` which imports `worldsim` internally). There is no standalone `pip install oscar_diffusers` path — clone the repo and install `requirements_minimal.txt` first.

Both wrappers produce bit-identical output at N=5 sampling steps (verified — SHA256 `e9bd9430d9828f4d` on `agibot_465` across all three surfaces).

```python
# diffusers-style
from oscar_diffusers import OSCARDiffusersPipeline
pipe = OSCARDiffusersPipeline.from_pretrained("zywu2115/OSCAR-2B")
out  = pipe.from_assets("agibot_465")(seed=42)
# out.frames is a list of np.uint8 (480, 640, 3) RGB arrays
```

```python
# DiffSynth-Studio-style
from oscar_diffsynth import OSCARDiffSynthPipeline
pipe = OSCARDiffSynthPipeline.from_dcp("checkpoints")
out  = pipe(
    first_frame="path/to/first.png",
    skeleton_video="path/to/gripper_scenario.mp4",
    prompt="robot grasps the bottle",
    num_inference_steps=35, guidance_scale=6.0, seed=42,
)
```

## Available cases (14)

| Embodiment   | Cases                                       |
|--------------|---------------------------------------------|
| `agibot`     | `agibot_465`, `agibot_360`                  |
| `droid`      | `droid_TRI`, `droid_AUTOLab_1202`           |
| `airoa`      | `airoa_ep000593`, `airoa_ep000719`          |
| `airoa_moma` | `airoa_moma_ep000755`, `airoa_moma_ep000963`|
| `interndata` | `interndata_ep936`, `interndata_ep1185`     |
| `rh20t_cfg5` | `rh20t_cfg5_103`, `rh20t_cfg5_0004`         |
| `rh20t_cfg7` | `rh20t_cfg7_34`, `rh20t_cfg7_0002`          |

Each `checkpoints/assets/<case>/` directory contains:

- `rgb.mp4` — ground-truth video (used by the dispatcher to extract the
  conditioning first frame at the case's `start_frame`).
- `gripper_scenario.mp4` — skeleton-conditioning input video.
- `caption.pickle` — text prompt source.

The dispatcher hardcodes the `start_frame` and per-case random seed values
needed to reproduce the bundled inference outputs.

## License

- Code: Apache-2.0 (see `LICENSE`).

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