| --- |
| 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](https://arxiv.org/abs/2502.09560) / [EB-ALFRED](https://huggingface.co/datasets/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](https://ai2thor.allenai.org/) 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 |
|
|
| ```python |
| 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](https://huggingface.co/datasets/EmbodiedBench/EB-ALFRED), which itself is a subset of [ALFRED](https://askforalfred.com/) (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`](https://huggingface.co/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**: |
| - [EmbodiedBench](https://github.com/EmbodiedBench/EmbodiedBench) — see upstream repo |
| - [ALFRED](https://github.com/askforalfred/alfred) — MIT |
| - [AI2-THOR](https://github.com/allenai/ai2thor) (used to render the images) — Apache 2.0 |
|
|
| If you redistribute, retain attribution to all three upstream sources. |
|
|
| ### Citation Information |
|
|
| ```bibtex |
| @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](https://huggingface.co/datasets/omniagentbench/EmbodiedBench-Audio). |
|
|
| Part of the [OmniAgentBench](https://huggingface.co/datasets/omniagentbench/OmniAgentBench) project. |
|
|
