--- configs: - config_name: bridge_high_forward data_files: - split: train path: bridge_high_forward/train-* - config_name: bridge_high_inverse data_files: - split: train path: bridge_high_inverse/train-* - config_name: bridge_low_forward data_files: - split: train path: bridge_low_forward/train-* - config_name: bridge_low_inverse data_files: - split: train path: bridge_low_inverse/train-* - config_name: bridge_mid_forward data_files: - split: train path: bridge_mid_forward/train-* - config_name: bridge_mid_inverse data_files: - split: train path: bridge_mid_inverse/train-* - config_name: droid_high_forward data_files: - split: train path: droid_high_forward/train-* - config_name: droid_high_inverse data_files: - split: train path: droid_high_inverse/train-* - config_name: droid_low_forward data_files: - split: train path: droid_low_forward/train-* - config_name: droid_low_inverse data_files: - split: train path: droid_low_inverse/train-* - config_name: droid_mid_forward data_files: - split: train path: droid_mid_forward/train-* - config_name: droid_mid_inverse data_files: - split: train path: droid_mid_inverse/train-* - config_name: rt1_high_forward data_files: - split: train path: rt1_high_forward/train-* - config_name: rt1_high_inverse data_files: - split: train path: rt1_high_inverse/train-* license: mit task_categories: - visual-question-answering language: - en tags: - robotics - embodied-ai - vision-language-models - action-understanding - spatial-reasoning pretty_name: 'ActionEQA ' size_categories: - 1K

ActionEQA: Action Interface for Embodied Question Answering

[![Project Page](https://img.shields.io/badge/Project-Page-blue?logo=googlechrome&logoColor=white)](https://actioneqa.github.io/) [![Paper](https://img.shields.io/badge/Paper-PDF-red?logo=arxiv&logoColor=white)](https://openreview.net/pdf?id=HY2ruqdMt4) [![OpenReview](https://img.shields.io/badge/OpenReview-Forum-green?logo=openreview&logoColor=white)](https://openreview.net/forum?id=HY2ruqdMt4) [![GitHub](https://img.shields.io/badge/GitHub-Repo-black?logo=github&logoColor=white)](https://github.com/ActionEQA/ActionEQA_TMLR_2026) **[Tianwei Bao](https://tianweibao.github.io/)**1*  ·  **[Qineng Wang](https://qinengwang-aiden.github.io/)**1*  ·  **[Kangrui Wang](https://jameskrw.github.io/)**1  ·  **[Mingkai Deng](https://mingkaid.github.io/)**2  ·  **[Guangyi Liu](https://github.com/guangyliu)**5  ·  **[Jiayuan Mao](https://jiayuanm.com/)**3
**[Larry Birnbaum](https://www.mccormick.northwestern.edu/research-faculty/directory/profiles/birnbaum-larry.html)**1  ·  **[Zhiting Hu](http://zhiting.ucsd.edu/)**4  ·  **[Eric P. Xing](https://mbzuai.ac.ae/study/faculty/professor-eric-xing/)**2,5  ·  **[Zhaoran Wang](https://zhaoranwang.github.io/)**1  ·  **[Manling Li](https://limanling.github.io/)**1 1 Northwestern University     2 Carnegie Mellon University     3 UPenn
4 UC San Diego     5 MBZUAI * Equal contribution > **ActionEQA** is the **first action-centric Embodied Question Answering (EQA) benchmark** designed to systematically evaluate the ability of Vision-Language Models (VLMs) to bridge the **semantic-to-physical gap**: translating high-level semantic instructions into precise low-level physical robot actions. ## Overview A pivotal challenge for embodied agents is bridging the *semantic-to-physical gap*: translating abstract goals (the "what") into the precise motor commands required for physical interaction (the "how"). Existing benchmarks focus on high-level perception and planning, failing to capture the depth and nature of this divide. ActionEQA addresses this with two core design principles: ### 1. Three-Tiered Action Hierarchy Actions are decomposed into three levels of abstraction: | Level | Symbol | Description | Example | |-------|--------|-------------|---------| | **High** | `a_high` | Natural language goal — the *"what"* | `"Close the Microwave"` | | **Mid** | `a_mid` | Semantic motion description — the *"semantic how"* | `"Move along positive X direction, rotate clockwise around Z-axis"` | | **Low** | `a_low` | Raw 7-DoF end-effector command — the *"physical how"* | `[Δx, Δy, Δz, Δroll, Δpitch, Δyaw, Δgripper]` | ### 2. Bidirectional Reasoning Framework Each action level is evaluated in two complementary directions: | Task | Direction | Given | Predict | |------|-----------|-------|---------| | **State Prediction (SP)** | Forward | Initial state `s_t` + action `a_t` | Resulting state `s_{t+1}` | | **Action Inference (AI)** | Backward | Initial state `s_t` + final state `s_{t+1}` | Action `a_t` that caused the transition | These two principles combine to yield **6 evaluation categories**: H-SP, H-AI, M-SP, M-AI, L-SP, L-AI. --- ## Dataset Statistics | Statistic | DROID | BridgeData V2 | RT-1 | **Total** | |-----------|-------|---------------|------|-----------| | Questions | 2,953 | 4,732 | 1,110 | **8,795** | | Unique Images | 8,026 | 14,146 | 4,144 | **26,213** | | Unique Episodes | 367 | 1,707 | 555 | **2,629** | ### Questions by Category | Category | Description | Count | |----------|-------------|-------| | H-SP | High-Level State Prediction | 1,891 | | H-AI | High-Level Action Inference | 1,891 | | M-SP | Mid-Level State Prediction | 947 | | M-AI | Mid-Level Action Inference | 1,379 | | L-SP | Low-Level State Prediction | 947 | | L-AI | Low-Level Action Inference | 1,740 | | **Total** | | **8,795** | > **Note:** RT-1 is used only for high-level tasks (H-SP and H-AI) because its mobile base introduces confounds for mid/low-level action analysis. --- ## Dataset Configurations Each configuration name follows the pattern: `{source}_{level}_{task}`. | Component | Values | Meaning | |-----------|--------|---------| | `source` | `bridge`, `droid`, `rt1` | Source robotics dataset | | `level` | `high`, `mid`, `low` | Action hierarchy level | | `task` | `forward`, `inverse` | State Prediction (`forward`) or Action Inference (`inverse`) | **Available configs:** | Config | Task | Source | |--------|------|--------| | `bridge_high_forward` | High-Level State Prediction | BridgeData V2 | | `bridge_high_inverse` | High-Level Action Inference | BridgeData V2 | | `bridge_mid_forward` | Mid-Level State Prediction | BridgeData V2 | | `bridge_mid_inverse` | Mid-Level Action Inference | BridgeData V2 | | `bridge_low_forward` | Low-Level State Prediction | BridgeData V2 | | `bridge_low_inverse` | Low-Level Action Inference | BridgeData V2 | | `droid_high_forward` | High-Level State Prediction | DROID | | `droid_high_inverse` | High-Level Action Inference | DROID | | `droid_mid_forward` | Mid-Level State Prediction | DROID | | `droid_mid_inverse` | Mid-Level Action Inference | DROID | | `droid_low_forward` | Low-Level State Prediction | DROID | | `droid_low_inverse` | Low-Level Action Inference | DROID | | `rt1_high_forward` | High-Level State Prediction | RT-1 | | `rt1_high_inverse` | High-Level Action Inference | RT-1 | --- ## Data Schema ### State Prediction (`*_forward`) — Forward Task Given an initial state and an action description, select the correct resulting state from 4 image candidates. | Field | Type | Description | |-------|------|-------------| | `frame1` | Image | Initial state `s_t` | | `action` | string | Action description (NL goal / semantic motion / 7-DoF vector) | | `option_A` | Image | Candidate resulting state A | | `option_B` | Image | Candidate resulting state B | | `option_C` | Image | Candidate resulting state C | | `option_D` | Image | Candidate resulting state D | | `correct_ans` | ClassLabel | Ground-truth answer: one of `A`, `B`, `C`, `D` | ### Action Inference (`*_inverse`) — Backward Task Given before and after states, select the correct action from 4 candidates. | Field | Type | Description | |-------|------|-------------| | `frame1` | Image | Before state `s_t` | | `frame2` | Image | After state `s_{t+1}` | | `option_A` | string | Candidate action A (NL description / motion label / 7-DoF vector) | | `option_B` | string | Candidate action B | | `option_C` | string | Candidate action C | | `option_D` | string | Candidate action D | | `correct_ans` | ClassLabel | Ground-truth answer: one of `A`, `B`, `C`, `D` | --- ## Usage ```python from datasets import load_dataset # High-Level State Prediction — BridgeData V2 ds = load_dataset("TianweiBao/ActionEQA", "bridge_high_forward", split="train") # High-Level Action Inference — DROID ds = load_dataset("TianweiBao/ActionEQA", "droid_high_inverse", split="train") # Mid-Level State Prediction — BridgeData V2 ds = load_dataset("TianweiBao/ActionEQA", "bridge_mid_forward", split="train") # Low-Level Action Inference — DROID ds = load_dataset("TianweiBao/ActionEQA", "droid_low_inverse", split="train") # Inspect a sample sample = ds[0] print("Before state:", sample["frame1"]) # PIL Image print("After state:", sample["frame2"]) # PIL Image print("Options:", sample["option_A"], sample["option_B"], sample["option_C"], sample["option_D"]) print("Correct answer:", sample["correct_ans"]) ``` ### Load all configs at once ```python from datasets import load_dataset configs = [ "bridge_high_forward", "bridge_high_inverse", "bridge_mid_forward", "bridge_mid_inverse", "bridge_low_forward", "bridge_low_inverse", "droid_high_forward", "droid_high_inverse", "droid_mid_forward", "droid_mid_inverse", "droid_low_forward", "droid_low_inverse", "rt1_high_forward", "rt1_high_inverse", ] datasets = {cfg: load_dataset("TianweiBao/ActionEQA", cfg, split="train") for cfg in configs} ``` --- ## Citation If ActionEQA is useful for your research, please cite: ```bibtex @article{bao2026actioneqa, title = {ActionEQA: Action Interface for Embodied Question Answering}, author = {Bao, Tianwei and Wang, Qineng and Wang, Kangrui and Deng, Mingkai and Liu, Guangyi and Mao, Jiayuan and Birnbaum, Larry and Hu, Zhiting and Xing, Eric P. and Wang, Zhaoran and Li, Manling}, journal = {Transactions on Machine Learning Research}, year = {2026}, url = {https://openreview.net/forum?id=HY2ruqdMt4} } ``` --- ## License ActionEQA is built on top of [DROID](https://droid-dataset.github.io/), [BridgeData V2](https://rail-berkeley.github.io/bridgedata/), and [RT-1](https://robotics-transformer1.github.io/). Please refer to the respective dataset licenses for terms of use.