RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation
Hao Li, Ziqin Wang, Zi-han Ding, Shuai Yang, Yilun Chen, Yang Tian, Xiaolin Hu, Tai Wang, Dahua Lin, Feng Zhao, Si Liu, Jiangmiao Pang

TL;DR
RoboInter introduces a comprehensive suite of datasets, benchmarks, and models with diverse intermediate representations to improve generalization and reasoning in robotic manipulation tasks.
Contribution
It provides the RoboInter Manipulation Suite, including large-scale annotated data, benchmarks for embodied reasoning, and a plan-then-execute framework for robotic manipulation.
Findings
Over 230k episodes with dense annotations across 571 scenes.
New benchmarks for spatial and temporal embodied VQA.
A modular plan-then-execute VLA framework supporting intermediate supervision.
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…
Peer Reviews
Decision·ICLR 2026 Poster
This paper makes important systematic contributions to the field of robotic manipulation. Originality: It represents the first large-scale systematic effort to address the data scarcity problem of intermediate representations in robotic manipulation, with RoboInter-Tool providing an innovative semi-automatic annotation solution. Quality: High-quality annotations for 200,000 videos are ensured through human verification and multi-round cross-validation, with comprehensive experimental design cove
The accuracy verification of camera parameter estimation and 2D-3D projection is insufficient, lacking quantitative analysis of inter-annotator consistency and failure case studies. Meanwhile, current experiments are primarily based on the Franka robotic platform, lacking cross-platform validation and generalization capability evaluation in more complex scenarios, with limited scale and diversity in real-world experiments. The systematic ablation studies on F-CoT design and various intermediate
1. The introduced RoboInter-Data is large-scale, including 200k episodes, 91M frames, 571 scenes. 2. This paper offers rich intermediate representation categories (sub-tasks, skills, affordances, boxes, traces, contact frames, etc.) during constructing RoboInter-VQA. These representations are critical for improving the abilities (like spatial intelligence) of embodied brain and explaination during bulding VLA systems. 3. This paper also introduces a full toolchain (RoboInter-Tool): an open-sou
1. The paper does not introduce architectural or training-paradigm innovations for vision-language-action (VLA) models. Its contributions mainly lie in demonstrating that the proposed intermediate representations can be effective within existing VLA and plan-then-execute frameworks. 2. While the paper mentions prior works that employ explicit chain-of-thought (EC) representations (e.g., ECoT), it does not provide sufficient discussion or comparative analysis with these conceptually related meth
- [S1] The paper proposes a new data, a benchmark, and a VLA model for embodied reasoning, which could be a great resource for the community. - [S2] Experiments validate the effectiveness of the collected data on both other benchmarks and the proposed benchmark. - [S3] The paper is well-written and easy to understand.
- [W1] While a plan-then-execute framework improves reasoning and interpretability by generating intermediate representations, it inherently hurts the latency of robotic systems. The paper does not provide any analysis regarding the inference speed of RoboInter-VLA compared with other VLA models. This could improve the understanding of the trade-off between reasoning capabilities and real-time performance in robotic systems.
Code & Models
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Taxonomy
TopicsMultimodal Machine Learning Applications · Robot Manipulation and Learning · Social Robot Interaction and HRI
