Robointer Vqa: Visual Question Answering Dataset for Robotic Manipulation

RobotInter-VQA is a Visual Question Answering dataset for robotic manipulation, developed as part of the RoboInter project. It covers generation and understanding of Intermediate Representations for task planning and is built on annotations from RoboInter-Data, with raw robot datasets sourced from DROID and RH20T. The dataset was created by InternRobotics and was last updated on February 14, 2026.

Use Cases

• Training models for visual question answering based on robotic manipulation scenes.
• Benchmarking intermediate representation generation for robotic task planning.
• Developing and evaluating multimodal AI systems that combine vision and language for robotics.

Strengths

• Dataset is built on established raw robot datasets DROID and RH20T.
• Focuses on both generation and understanding of Intermediate Representations, a key concept for manipulation.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

InternRobotics

Collection Method

Built on annotations of RoboInter-Data, with raw data from DROID and RH20T datasets.

Freshness

Last updated 2026-02-14 03:25:26; freshness should be verified.