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EmbodiedBench-Audio

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metadata 
license: mit
language:
  - en
pretty_name: EmbodiedBench-Audio (clean speech, 300 samples)
size_categories:
  - n<1K
task_categories:
  - text-to-speech
  - automatic-speech-recognition
  - visual-question-answering
  - robotics
multilinguality:
  - monolingual
annotations_creators:
  - machine-generated
  - crowdsourced
language_creators:
  - crowdsourced
source_datasets:
  - extended|EmbodiedBench/EB-ALFRED
  - extended|alfred
tags:
  - embodiedbench
  - alfred
  - ai2thor
  - speech
  - multimodal
  - omniagentbench
  - embodied-agents
  - household-tasks
configs:
  - config_name: default
    data_files:
      - split: test
        path: clean_embodied/Embodied_task.csv

Dataset Card for EmbodiedBench-Audio (clean_embodied)

Dataset Summary

EmbodiedBench-Audio is the speech-modality extension of the EmbodiedBench / EB-ALFRED benchmark. This release (clean_embodied/) provides 300 paired examples of (audio, image, instruction, ground-truth plan) covering household embodied tasks in AI2-THOR scenes.

Each sample comes with:

  • Clean text-to-speech audio of the natural-language goal
  • The starting-frame screenshot the agent sees
  • The original written goal and ground-truth high-level plan (both natural-language step descriptions and symbolic PDDL actions)

The folder is named clean_embodied/ so contributors can drop sibling folders like noisy_embodied_<env>/ later without overwriting the clean baseline.

Supported Tasks

  • Embodied speech grounding — predict the action plan from spoken goal + image
  • Speech-to-PDDL plan generation — speech input → symbolic action sequence
  • ASR robustness — comparing models that transcribe-then-plan vs. models that ingest audio directly
  • Cross-modal evaluation — audio vs. text input on identical samples

Languages

English (en, US accent, single TTS voice).

Dataset Structure

Layout 

clean_embodied/
├── README.md                          (this card)
├── Embodied_task.csv                  300 rows · 7 columns — source of truth
├── audio/
│   ├── manifest.json                  sample_id → all CSV fields + audio_file + spoken_text
│   └── embodied_<trial>_<repeat_idx>.wav   300 files · 24 kHz mono · ~35 MB
└── images/
    └── <trial>_<repeat_idx>.png       300 files · ~33 MB · AI2-THOR scene capture

Data Fields (Embodied_task.csv)

Field Type Description
trial string Original ALFRED trial id, e.g. trial_T20190908_044113_026049
repeat_idx int Annotator index; each trial has 3+ paraphrasings of the same goal by different turkers
scene string AI2-THOR floor plan, e.g. FloorPlan219
goal_instr string One-sentence high-level goal — the input text the agent receives
gt_high_descs list[string] (JSON-encoded) Step-by-step natural-language descriptions — auxiliary annotation
gt_high_pddl_actions list[string] (JSON-encoded) Symbolic ground-truth plan, e.g. ["GotoLocation ['dresser']", "PickupObject ['cellphone']", ...]
image_name string Filename of the starting frame in images/

Data Fields (audio/manifest.json)

For every sample id, the manifest records:

Key Description
sample_id embodied_<trial>_<repeat_idx>
audio_file <sample_id>.wav
spoken_text Identical to goal_instr — the text fed to the TTS model
trial, repeat_idx, scene, image_name, gt_high_descs, gt_high_pddl_actions Mirrored from CSV for convenience

Pairing across modalities 

sample_id  = f"embodied_{trial}_{repeat_idx}"
audio_path = f"clean_embodied/audio/{sample_id}.wav"
image_path = f"clean_embodied/images/{trial}_{repeat_idx}.png"

Splits

A single test split with 300 examples. The CSV is a curated subset of EB-ALFRED's valid_seen / valid_unseen splits sampled across multiple ALFRED task types (e.g. look_at_obj_in_light, pick_and_place_simple, pick_clean_then_place_in_recep, pick_heat_then_place_in_recep, pick_cool_then_place_in_recep).

Dataset Creation

Curation Rationale

EmbodiedBench evaluates language-conditioned embodied agents on text + image, but the original benchmark has no audio modality. This release adds one: each goal instruction is rendered as clean speech so models with native audio input (Qwen2.5-Omni, Qwen3-Omni, etc.) can be evaluated in the same protocol. The 300-sample subset balances coverage across ALFRED task types while staying small enough for fast iteration.

Source Data

  • Goal instructions, step descriptions, PDDL plans: EB-ALFRED, which itself is a subset of ALFRED (Shridhar et al., CVPR 2020). The natural-language fields were collected via Amazon Mechanical Turk on ALFRED trajectories.
  • Images: Starting frames extracted from AI2-THOR scene trajectories included in EB-ALFRED.
  • Subset selection: 300 trial/annotator pairs sampled by the OmniAgentBench team to span ALFRED task types.

Annotations

  • Audio: Machine-generated. TTS via Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign on a single NVIDIA RTX PRO 6000 Blackwell GPU. Voice instruct prompt: "Speak naturally and clearly as if giving a household task instruction to a robot assistant." Output: 24 kHz mono WAV, bf16 inference, no flash-attn. Generated 2026-05-03; ~10 min wall time for 300 samples.
  • All other fields (text, plans, scene, image): inherited unmodified from EB-ALFRED / ALFRED.

Personal and Sensitive Information

None. All instructions describe household task goals with no identifiable people, addresses, or PII.

Audio Characteristics

  • Sample rate / channels: 24 kHz · mono
  • Duration: 1.2 s – 5.1 s per WAV (median 2.3 s, mean 2.5 s)
  • Speech rate: ~208 wpm — fast, because source text is short (median 9 words)
  • Voice: Single Qwen3-TTS-VoiceDesign voice across all 300 samples — no speaker variation in this clean release

Considerations for Using the Data

Limitations

  • One TTS voice, no speaker variation. All 300 samples use the same synthetic voice. Results should not be interpreted as covering speaker diversity.
  • Short audio. Goal instructions are one-sentence (median 9 words), so audio is brief. Models that benefit from longer context windows may not fully exercise their capacity here.
  • Speech rate is fast. ~208 wpm vs. natural English ~150 wpm. The TTS does not include expressive prosody beyond what the voice-design instruct prompt nudges.
  • Audio is fully synthetic. No human-recorded speech. Add noisy_*/ siblings (background noise, reverb, accents, real recordings) to extend robustness coverage.

Biases & Social Impact

The underlying ALFRED instructions describe everyday household chores in stereotypical kitchen/bedroom/bathroom layouts and reflect the cultural framing of the original Mechanical Turk annotators. Models trained or evaluated on this data will inherit those framings.

Other Known Issues

  • The 300-sample subset is a convenience sample, not a stratified random sample, and is not balanced for AI2-THOR scene type or PDDL plan length.

Additional Information

Dataset Curators

OmniAgentBench team (audio synthesis, packaging, curation of the 300-sample subset). Upstream natural-language and trajectory data: EmbodiedBench team (Yang et al., 2025) and ALFRED team (Shridhar et al., 2020).

Licensing Information

Released under MIT, subject to the licenses of the upstream datasets:

If you redistribute, retain attribution to all three upstream sources.

Citation Information 

@misc{omniagentbench_embodied_audio_2026,
  title  = {EmbodiedBench-Audio: clean speech for the EmbodiedBench 300-sample subset},
  author = {OmniAgentBench Team},
  year   = {2026},
  url    = {https://huggingface.co/datasets/omniagentbench/EmbodiedBench-Audio}
}

@inproceedings{yang2025embodiedbench,
  title  = {EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents},
  author = {Yang, Rui and others},
  year   = {2025},
  eprint = {2502.09560},
  archivePrefix = {arXiv}
}

@inproceedings{shridhar2020alfred,
  title     = {ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks},
  author    = {Shridhar, Mohit and Thomason, Jesse and Gordon, Daniel and Bisk, Yonatan and Han, Winson and Mottaghi, Roozbeh and Zettlemoyer, Luke and Fox, Dieter},
  booktitle = {CVPR},
  year      = {2020}
}

Contributions

Contributions of noisy_<env>/, wild_<variant>/, or larger-scale follow-ups are welcome — the repo is intentionally laid out so new sibling folders sit cleanly alongside clean_embodied/. Open a discussion or PR on the HuggingFace dataset page.

Part of the OmniAgentBench project.