Datasets:
metadata
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<n<10K
dataset_info:
- config_name: bridge_high_forward
features:
- name: frame1
dtype: image
- name: action
dtype: string
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- name: option_B
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- name: option_C
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- name: option_D
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- name: correct_ans
dtype:
class_label:
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'1': B
'2': C
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- config_name: droid_high_forward
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ActionEQA: Action Interface for Embodied Question Answering
Tianwei Bao1* · Qineng Wang1* · Kangrui Wang1 · Mingkai Deng2 · Guangyi Liu5 · Jiayuan Mao3
Larry Birnbaum1 · Zhiting Hu4 · Eric P. Xing2,5 · Zhaoran Wang1 · Manling Li1
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
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
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:
@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, BridgeData V2, and RT-1. Please refer to the respective dataset licenses for terms of use.