Datasets:

AetherLabs-AI
/

RobotAgentBench-TrainingData

pretty_name: RobotAgentBench Training Data
language:
  - en
task_categories:
  - robotics
size_categories:
  - 1M<n<10M
tags:
  - robotics
  - lerobot
  - robotagentbench
  - arxiv:2610.34649
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/chunk-*/*.parquet

RobotAgentBench

Paper · Project · Code · Model

RobotAgentBench: Towards Open-world Robot Autonomy introduces a benchmark for evaluating robot agents in realistic, open-world household environments. An agent-assisted, human-in-the-loop real-to-sim pipeline constructs household environments with interactive objects and physically grounded tasks.

The task hierarchy comprises atomic tasks, short-horizon tasks, long-horizon tasks, and open-ended scenarios. Evaluation spans task discovery, context reasoning, manipulation, failure recovery, robustness, and autonomous fulfillment of high-level user needs.

Dataset summary

Category	Episodes	Frames	Hours
Atomic	4,195	3,151,810	43.775139
Short-horizon	750	2,475,637	34.383847
Long-horizon	70	971,943	13.499208
Open-ended	40	1,520,713	21.121014
Total	5,055	8,120,103	112.779208

The release contains 15,165 camera videos and 203 language instructions. The train split contains all 5,055 episodes. Tablecloth-manipulation tasks are excluded.

Per-source task counts · Episode duration statistics

Data format

Demonstrations use LeRobot v2.1 at 20 Hz.

Feature	Type / shape	Content
`observation.state`	`float32[23]`	Twenty joint channels, world-frame base `(x, y)`, and unwrapped yaw
`action`	`float32[22]`	Twenty joint-position targets, body-forward velocity, and yaw rate
`observation.images.top_head`	RGB video, native resolution	Head camera
`observation.images.hand_left`	RGB video, native resolution	Left wrist camera
`observation.images.hand_right`	RGB video, native resolution	Right wrist camera
`task_index`	`int64`	Language instruction in `meta/tasks.jsonl`
`timestamp`	`float64`	Episode time in seconds
`episode_index`, `frame_index`, `index`	`int64`	Episode, frame, and dataset indices

Torso lift and base position use metres; angular joint channels and yaw use radians. Base commands use metres per second and radians per second. Channel order and units are recorded in meta/info.json.

data/        Episode data in Parquet format
videos/      Head and wrist camera videos
meta/        Tasks, episodes, features, statistics, and provenance
auxiliary/   Original source fields and supporting data

Episode provenance and original diagnostic labels are retained in meta/episode_provenance.jsonl and meta/qualification.jsonl. Signal origins and alignment are recorded in meta/component_origin_index.json and meta/alignment_conventions.json.

Native video resolution and browser playback

In progress: 751/1,329 targeted videos corrected. See repair status.

Videos retain their source camera resolution. Synthetic black padding is removed without resizing camera content; examples include 640 × 480, 1280 × 800 and 848 × 480. Actual per-episode dimensions are listed in video dimensions. Spatial dimensions are null in global feature metadata when episodes have different sizes. The portable loader resolves a selected episode's concrete shapes in memory. For mixed-resolution training batches, explicitly configure your model's image preprocessing or group episodes by resolution; padding is not reintroduced by this dataset.

RGB H.264 streams with unspecified color primaries/transfer tags could render green in Chrome. Corrected files declare full-range RGB, BT.709 primaries and the sRGB transfer function. Every corrected video is fully decoded and its retained RGB pixels are compared against the previous release, with exact equality and unchanged frame timestamps. Some unaligned crops require lossless RGB re-encoding; most files use stream-copy metadata edits. Numeric action/state data, task identities and original qualification annotations are unchanged. This repair is presentation validation, not new physical-success or owner approval.

The previous presentation remains available at revision cfdc72460733616ad216db60410d2c49dbf4e832. There is no archive to unpack on Hugging Face; the files under videos/ are individual MP4s.

Download

hf download AetherLabs-AI/RobotAgentBench-TrainingData --repo-type dataset --local-dir RobotAgentBench

Load

python -m pip install "lerobot==0.3.3" av
cd RobotAgentBench
python portable_loader.py --root . --episode 0 --frame 0 --cache-dir ../rab-cache

Citation

@misc{mao2026robotagentbenchopenworldrobotautonomy,
      title={RobotAgentBench: Towards Open-world Robot Autonomy}, 
      author={Lingjun Mao and Jinglin Cao and Lukun He and Wenpeng Xu and Yuchen Yan and Hengyu Jin and Zijun Zhang and Jinzhou Tang and Songyao Jin and Ziche Liu and Yifei Shao and Junbo Huang and Fang Nan and Ruobing Han and Ziqiao Xi and Ziming Xu and Shuang Liang and Sibo Zhu and Wenyi Wu and Xinyue Wang and Yiming Huang and Xinkai Zou and Gao Zhu and Kun Zhou and Biwei Huang},
      year={2026},
      eprint={2610.34649},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2610.34649}, 
}