InternRobotics/RoboInter-VQA

RobotInter-VQA: Intermediate Representation Understanding & Generation VQA Dataset for Manipulation

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A Visual Question Answering dataset for robotic manipulation, developed as part of the RoboInter, covering generation, understanding (and task planning) of Intermediate Representations. The dataset is built on top of the annotation of RoboInter-Data, raw robot datasets are from DROID and RH20T.

Dataset Structure

robotinter/
├── Generation/          # generation tasks (grounding, trajectory, contact, ...)
│   ├── image/           # Images (zip archives, extract before use)
│   │   ├── train/{droid,rh20t}/
│   │   └── val/
│   └── meta/            # VQA annotations in JSON
│       ├── train/{droid,rh20t}/{origin_format,llava_format,smart_resize_format}/
│       └── val/{origin_format,llava_format,smart_resize_format}/
├── Understanding/       # understanding tasks (multiple-choice/decision)
│   ├── image/
│   │   ├── train/{droid,rh20t}/
│   │   └── val/
│   └── meta/
│       ├── train/{droid,rh20t}/
│       └── val/
└── Task_planning/       # Task planning & primitive recognition
    ├── image/
    │   ├── train/manipvqa/
    │   └── val/{planning,choice,decide}/
    └── meta/
        ├── train/manipvqa/
        └── val/{planning,choice,decide}/

Quick Start

1. Extract images: All images are stored as .zip files. Extract them in place:

   cd RoboInter-VQA/Task_planning/image/train/manipvqa
   cat task_planning.zip.* > task_planning_full.zip
   cd ../../../../
   find . -name "*.zip" -execdir unzip -o {} \;
   

2. Load VQA data: Refer to RoboInterVLM.

Coordinate Formats (Generation only)

The Generation annotations are provided in three coordinate formats. The underlying data and images are identical; only the coordinate representation in the answers differs:

Format	Description	Example
origin_format	Pixel coordinates in original image resolution (h x w)	[[72, 102], [192, 179]]
llava_format	Normalized coordinates in [0, 1] range as LLaVA-based Model	[[0.22, 0.57], [0.60, 0.99]]
smart_resize_format	Pixel coordinates in resized image resolution as Qwen-based Model (new_h x new_w)	[[69, 95], [184, 167]]

JSON Descriptions

Generation (7 task types)

Each entry follows this schema:

{
  "id": "unique_sample_id",
  "task": "task_type",
  "conversations": [{"from": "human", "value": "..."}, {"from": "gpt", "value": "..."}],
  "images": "relative/path/to/image.jpg",
  "gt": "ground_truth_value",
  "h": 180, "w": 320,
  "new_h": 168, "new_w": 308
}

JSON File	Task	Description	Output Format
*_traj_qa.json	Trajectory Prediction	Given a task description and a starting position, predict 10 future trajectory waypoints for the gripper.	{"future_traj": [[x1,y1], ...]}
*_traj_qa_wo_init_pos.json	Trajectory Prediction (no init pos)	Same as above but without providing the starting position in the prompt.	{"future_traj": [[x1,y1], ...]}
*_gripper_det_qa.json	Gripper Detection	Detect the current bounding box of the robot gripper in the scene.	{"gripper_det_bbox": [[x1,y1],[x2,y2]]}
*_contact_point_qa.json	Contact Point Prediction	Predict the two contact points where the gripper fingers touch the manipulated object.	{"contact_point": [[x1,y1],[x2,y2]]}
*_contact_box_qa.json	Contact Box Prediction	Predict the bounding box of the gripper at the moment of contact with the object.	{"contact_bbox": [[x1,y1],[x2,y2]]}
*_current_box_qa.json	Current Object Box	Predict the current bounding box of the manipulated object.	{"current_bbox": [[x1,y1],[x2,y2]]}
*_final_box_qa.json	Final Object Box	Predict the final bounding box of the manipulated object (at the end of the action).	{"final_bbox": [[x1,y1],[x2,y2]]}

Understanding (6 task types)

Multiple-choice VQA tasks that evaluate visual understanding of intermediate representations. Each entry uses single-image or multi-choice concatenated images.

JSON File	Task	Description	Answer
contact_decide.json	Contact Decision	Given a scene, determine whether the gripper has reached/contacted the object.	Yes / No
grasppose_choice.json	Grasp Pose Choice	Select the correct grasping pose from 4 candidate images (A/B/C/D), where orange fork-like patterns represent possible gripper poses.	A/B/C/D
grounding_choice.json	Grounding Choice	Select which image correctly depicts the bounding box (purple box) of the manipulated object from 4 candidates.	A/B/C/D
traj_choice.json	Trajectory Choice	Select the correct gripper trajectory from 4 candidate images with gradient-colored paths (green=start, red=end).	A/B/C/D
trajlang_choice.json	Trajectory-Language Choice	Given a trajectory visualization, select the correct task description from 4 language options.	A/B/C/D
traj_direction_choice.json	Trajectory Direction Choice	Given colored arrows around the gripper, select which color represents the actual movement direction.	A/B/C/D

Task Planning (16 task types)

Multi-image (video frame) or single-frame VQA tasks for high-level task planning, including 16 types (Scene understanding, Discriminative affordance negative task, Discriminative affordance positive task, Future multitask selection, Future prediction task, Future primitive selection task, Generative affordance task, Past description task, Past multitask selection, Past primitive selection, Planning remaining steps task, Planning task, Planning with context task, Success negative task, Success positive task, Temporal understanding). Each entry uses 8 sampled frames as input (Temporal understanding and Scene understanding use a combined image of four image). We provide 4 examples below:

JSON File	Task	Description	Answer
train/manipvqa/task_planning.json	Next Step Planning (train)	Given 8 video frames and a goal, predict the next sub-task to perform.	Free-form text
val/planning/task_planning.json	Next Step Planning (val)	Same as training but on held-out validation data.	Free-form text
val/choice/task_planning.json	Primitive Selection (val)	Given 8 video frames, select which action primitive was just executed from 4 options.	A/B/C/D
val/decide/task_planning.json	Success Decision (val)	Given 8 video frames and a sub-task description, determine whether the sub-task was successfully completed.	Yes / No

Data Statistics

Generation

Source	traj_qa	gripper_det	contact_point	contact_box	current_box	final_box
DROID (train)	31,282	84,777	78,004	78,004	149,671	145,996
RH20T (train)	33,803	120,747	115,266	115,266	225,055	224,944
Val	2,000	2,000	2,000	2,000	2,000	2,000

Understanding

Source	contact_decide	grasppose_choice	grounding_choice	traj_choice	trajlang_choice	traj_direction
RH20T (train)	15,060	9,835	8,158	3,610	3,610	3,729
DROID (train)	18,184	-	57,572	8,245	8,245	6,500
Val	15,514	2,702	6,108	787	1,474	266

Task Planning

Split	Entries
Train (manipvqa)	928,819
Val - planning	10,806
Val - choice	15,059
Val - decide	10,629

License

Please refer to the original dataset licenses for RoboInter, DROID and RH20T.