---
license: cc-by-4.0
task_categories:
- robotics
tags:
- robotics
- cross-embodiment
- imitation-learning
- robocasa
- lerobot
- humanoid
- franka
pretty_name: RoboCasa-DC
size_categories:
- 1K
- Paper:
- Code:
*Overview of RoboCasa-DC. For each of the 24 tasks, 100
Panda-arm trajectories are paired with GR-1 humanoid demonstrations for training. For
evaluation, each task has 50 pre-defined seeds, each with a GR-1 humanoid demonstration.*
## Benchmark construction
- **Training pairs.** For each task, 100 Panda-arm trajectories from RoboCasa are paired
with GR-1 humanoid demonstrations. Each demonstration was collected by restoring the
initial simulation state of its Panda-arm trajectory and teleoperating the humanoid in
the same scene with a Leap Motion controller.
- **Evaluation seeds and demonstrations.** Each task has 50 pre-defined evaluation seeds,
each with an environment in `eval_envs/` and a GR-1 humanoid demonstration collected
from the same initial scene state.
- **Demonstration camera view.** For each task, the GR-1 camera view that best captures
task execution was selected manually; it is the single camera listed in the task's
`meta/info.json`.
## Contents
| directory | contents | episodes |
|---|---|---|
| `panda_train//` | Panda-arm trajectories from RoboCasa: actions, proprioceptive states, videos from two static cameras and one wrist camera, end-effector traces | 24 tasks x 100 = 2400 |
| `gr1_train//` | GR-1 humanoid demonstrations paired with `panda_train`: one camera video, state/action | 2369 |
| `gr1_eval//` | GR-1 humanoid demonstrations for the evaluation seeds: one camera video, state/action | 24 tasks x 50 = 1200 |
| `eval_envs///` | Panda-arm simulation scene for each pre-defined evaluation seed | 24 tasks x 50 = 1200 |
## Pairing rule (by episode index)
Pairing is purely positional; there is no separate index file.
- **Training:** `panda_train//.../episode_XXXXXX` and
`gr1_train//.../episode_XXXXXX` with the same task and the same 6-digit episode
index form one training pair.
- **Evaluation:** seed directory `eval_envs///` pairs with
`gr1_eval//.../episode_{N:06d}`, which shares seed N's initial scene state. The
policy is conditioned on the GR-1 video of episode N and rolled out in the Panda-arm scene
of seed N. Each task has exactly 50 pre-defined seeds. The `gr1_eval` episode indices match the seed directories exactly.
## File formats
Episode files follow the GR00T / LeRobot v2 layout: one parquet and one mp4 per episode
per camera, `data/chunk-000/episode_XXXXXX.parquet` and
`videos/chunk-000/observation.images./episode_XXXXXX.mp4`. All videos are
20 fps; Panda videos are 128x128, GR-1 videos are 256x256. Frame `t` of the video matches
row `t` of the data and trace parquet.
### `panda_train//`
- `meta/modality.json` defines the named slices of the state and action vectors;
`meta/tasks.jsonl` maps `task_index` to the language instruction.
- `data/chunk-000/episode_XXXXXX.parquet`, one row per timestep:
| column | type | description |
|---|---|---|
| `observation.state` | list[53] | joint pos / cos / sin / vel, eef pos + quat, gripper qpos / qvel, base pos + quat, base-to-eef pos + quat (slices in `meta/modality.json`) |
| `action` | list[12] | delta eef position (3), delta eef rotation as axis-angle (3), gripper (1), mobile base (3), torso (1), base mode (1) |
| `annotation.human.action.task_description` | int64 | index into `meta/tasks.jsonl` (language instruction) |
| `annotation.human.validity` | int64 | index into `meta/tasks.jsonl` |
- `trace/episode_XXXXXX.parquet`: 2D end-effector traces, generated by projecting the
end-effector position into each of the two static camera views. Columns are a two-level
pandas MultiIndex (read with `pandas.read_parquet`):
`('eef_trace_robot0_agentview_left_pixel_rc', 'x' | 'y')` and
`('eef_trace_robot0_agentview_right_pixel_rc', 'x' | 'y')`, float32 image coordinates
normalized by image size. Values outside [0, 1] mean the end effector is off-screen in
that view.
### `gr1_train//` and `gr1_eval//`
- `meta/info.json` (paths, video shape, fps). `gr1_train` also has
`meta/camera_config.json`, the camera pose used to render the GR-1 videos.
- `data/chunk-000/episode_XXXXXX.parquet`: same columns as Panda, with
`observation.state` of length 221 and `action` of length 24 (GR-1 humanoid), plus an
int64 column `observation.images.` holding the frame index into the mp4.
The GR-1 splits have no `meta/tasks.jsonl`, so their annotation columns carry no
language; the instruction for a training pair is the Panda episode's.
### `eval_envs///`
- `environment.xml`: MuJoCo model of the Panda kitchen scene.
- `ep_meta.json`: RoboCasa episode metadata (layout / style ids, object and fixture
configs, language instruction, camera configs).
- `initial_state.npz`: `states`, the flattened MuJoCo simulation state to reset to.
## License
RoboCasa-DC is released under the
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
license. It is derived from [RoboCasa](https://robocasa.ai), whose assets and datasets are
also licensed under CC BY 4.0. The SeeTraceAct code is released separately on
[GitHub](https://github.com/jaehyeon-son/SeeTraceAct).
## Citation
If you use RoboCasa-DC, please cite:
```bibtex
@inproceedings{son2026seetraceact,
title = {SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos},
author = {Son, Jaehyeon and Kim, Junhyun and Kam, Kyle and Coholich, Jeremiah and Kim, Seok Joon and Kim, Jinhoo and Kim, Chris Dongjoo and Cho, Jaemin and Fox, Dieter and Kira, Zsolt},
booktitle = {Conference on Robot Learning (CoRL)},
year = {2026}
}
```