RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation

2026-02-10

AI Summary

This work addresses key limitations of current vision–language–action (VLA) models—namely, the high cost and scarcity of robotic manipulation datasets, strong embodiment dependency, limited coverage, and absence of intermediate representation supervision. To overcome these challenges, the authors present the first systematic framework for constructing diverse intermediate representations spanning both spatial and temporal dimensions. Leveraging a lightweight, semi-automatic GUI annotation tool, they introduce RoboInter-Data, a large-scale dataset comprising over 230,000 manipulation segments across 571 scenes, annotated with more than ten fine-grained intermediate representations. Complementing this resource, they propose the RoboInter-VQA evaluation benchmark and the RoboInter-VLA modeling framework. This integrated approach substantially enhances the generalization and embodied reasoning capabilities of VLA models, enabling both modular and end-to-end training within a planning–execution paradigm.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Language and Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Advances in large vision-language models (VLMs) have stimulated growing interest in vision-language-action (VLA) systems for robot manipulation. However, existing manipulation datasets remain costly to curate, highly embodiment-specific, and insufficient in coverage and diversity, thereby hindering the generalization of VLA models. Recent approaches attempt to mitigate these limitations via a plan-then-execute paradigm, where high-level plans (e.g., subtasks, trace) are first generated and subsequently translated into low-level actions, but they critically rely on extra intermediate supervision, which is largely absent from existing datasets. To bridge this gap, we introduce the RoboInter Manipulation Suite, a unified resource including data, benchmarks, and models of intermediate representations for manipulation. It comprises RoboInter-Tool, a lightweight GUI that enables semi-automatic annotation of diverse representations, and RoboInter-Data, a large-scale dataset containing over 230k episodes across 571 diverse scenes, which provides dense per-frame annotations over more than 10 categories of intermediate representations, substantially exceeding prior work in scale and annotation quality. Building upon this foundation, RoboInter-VQA introduces 9 spatial and 20 temporal embodied VQA categories to systematically benchmark and enhance the embodied reasoning capabilities of VLMs. Meanwhile, RoboInter-VLA offers an integrated plan-then-execute framework, supporting modular and end-to-end VLA variants that bridge high-level planning with low-level execution via intermediate supervision. In total, RoboInter establishes a practical foundation for advancing robust and generalizable robotic learning via fine-grained and diverse intermediate representations.

Problem

• robotic manipulation
• intermediate representation
• vision-language-action
• dataset limitation
• embodied reasoning

Innovation

• intermediate representation
• vision-language-action (VLA)
• embodied reasoning
• robotic manipulation
• semi-automatic annotation