OpenNovelty.org

RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
VLMLLMBenchmarkmanipulation
Abstract:

Large language and vision-language models have inspired end-to-end vision-language-action (VLA) systems in robotics, yet existing robot datasets remain costly, embodiment-specific, and insufficient, limiting robustness and generalization. Recent approaches address this by adopting a plan-then-execute paradigm, where high-level plans are generated before translating into low-level actions, but their success depends on fine-grained intermediate supervision that current datasets lack. To fill this gap, we present the RoboInter Manipulation Suite, a unified resource for data, benchmarking, and modeling of intermediate representations. It includes RoboInter-Tool, a lightweight GUI for semi-automatic per-frame annotation of embodied videos, and RoboInter-Data, a human-verified dataset with over 200k episodes across 571 diverse scenes, offering dense per-frame alignment across more than nine intermediate categories and surpassing prior work in both scale and quality. Building on this foundation, RoboInter-VQA introduces 8 spatial and 20 temporal embodied QA categories to benchmark and enhance the embodied capabilities of current large vision-language models, while RoboInter-VLA provides a flexible plan-then-execute framework with modular and end-to-end variants that link planning to execution. Together, these contributions establish RoboInter Manipulation Suite as a foundation for advancing generalizable and robust robotic learning through fine-grained intermediate supervision.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces RoboInter, a unified resource combining data annotation tools, a large-scale dataset with dense per-frame intermediate annotations, embodied VQA benchmarks, and a plan-then-execute VLA framework. It resides in the 'Unified and Holistic Representation Frameworks' leaf, which contains only three papers total. This is one of the sparsest leaves in the taxonomy, suggesting that comprehensive frameworks integrating multiple intermediate representation types remain relatively underexplored compared to specialized single-modality or single-task approaches.

The taxonomy reveals that most research concentrates in specialized directions: Visual and Multi-Modal Representation Learning (nine papers across three subcategories), Language-Grounded and Vision-Language Representations (seven papers), and Object-Centric and Structured Representations (ten papers across three subcategories). RoboInter's holistic approach contrasts with these focused efforts—it aims to bridge visual pretraining, language grounding, object-centric reasoning, and policy learning within a single framework. The taxonomy's scope_note for this leaf explicitly highlights integration of 'multiple intermediate representation types' and 'unified benchmarks and tooling,' positioning RoboInter as a synthesis effort rather than a specialized method.

Among 25 candidates examined, the dataset contribution (RoboInter-Data) shows one refutable candidate from six examined, suggesting some overlap with prior large-scale annotation efforts. The VQA benchmark (RoboInter-VQA) and VLA framework (RoboInter-VLA) show no refutable candidates among nine and ten examined respectively, indicating these components appear more novel within the limited search scope. The statistics suggest the data contribution faces more direct prior work, while the benchmark and framework components occupy less crowded territory, though this assessment is constrained by the top-25 semantic search scope.

Given the sparse population of the 'Unified and Holistic Representation Frameworks' leaf and the limited search scope, the work appears to occupy a relatively open research direction. However, the single refutable candidate for the dataset contribution and the modest search scale (25 candidates total) mean this assessment captures only a snapshot of the immediate semantic neighborhood, not an exhaustive field survey. The framework's integration of annotation tools, benchmarks, and modeling represents a systems-level contribution that may be harder to directly compare against specialized prior work.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
25
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: intermediate representation learning for robotic manipulation. The field organizes itself around several complementary perspectives on how robots should encode sensory input and task structure. Visual and Multi-Modal Representation Learning explores perceptual encodings from camera and tactile streams, often leveraging self-supervised pretraining (e.g., EmbodiedMAE[6], Multi View Masked[18]). Language-Grounded and Vision-Language Representations integrate linguistic instructions with visual observations to ground commands in perception (Language Driven Representation[1], RoboGround[34]). Object-Centric and Structured Representations decompose scenes into entities or keypoints (Dense Object Descriptors[24], Multi Object Keypoints[32]), while World Models and Predictive Dynamics learn forward models that anticipate future states (World Models Survey[5], DreamerRL[27]). Information-Theoretic and Latent Compression methods distill high-dimensional inputs into compact bottlenecks (Information Bottleneck[4], PEEK[3]), and Policy Learning with Intermediate Representations directly couples learned features to action selection. Domain Adaptation and Transfer Learning address sim-to-real gaps (Sim to Real Adaptation[17]), Active Perception and Embodied Interaction emphasize feedback loops between sensing and acting, and Adversarial Robustness and Security consider safety under perturbations (Adversarial Distillation[47]). A particularly active line of work focuses on Unified and Holistic Representation Frameworks that synthesize multiple modalities, task structures, and learning objectives into a single architecture. RoboInter[0] exemplifies this direction by proposing an integrated approach that combines visual, language, and action representations within a coherent framework, aiming to capture the full spectrum of manipulation-relevant information. This contrasts with more specialized efforts: PEEK[3] emphasizes information-theoretic compression to isolate task-relevant features, while Splat-MOVER[30] leverages 3D Gaussian splatting for spatially grounded scene understanding. The tension between holistic integration and modular specialization remains a central open question—whether a single unified encoder can match or exceed the performance of carefully tailored representations for distinct subtasks. RoboInter[0] sits squarely in the holistic camp, seeking to demonstrate that end-to-end learning over diverse data can yield representations that generalize broadly across manipulation scenarios.

Claimed Contributions

RoboInter-Data: Large-scale human-verified dataset with dense per-frame intermediate annotations

A large-scale manipulation dataset containing over 200,000 episodes across 571 scenes with human-verified, dense per-frame annotations of nine intermediate representation categories (subtasks, primitive skills, affordances, target points, gripper/object bounding boxes, traces, contact points, and placement affordance). This dataset surpasses prior work in both scale and annotation quality.

6 retrieved papers
Can Refute
RoboInter-VQA: Spatial and temporal embodied VQA benchmark and training data

A curated embodied visual question answering dataset and benchmark comprising 8 spatial and 20 temporal QA categories designed to systematically evaluate and improve the embodied reasoning and grounding capabilities of vision-language models in manipulation scenarios.

9 retrieved papers
RoboInter-VLA: Flexible plan-then-execute framework with modular and end-to-end variants

A flexible plan-then-execute framework that supports multiple architectural variants (implicitly-conditioned, explicitly-conditioned, and modular) for robotic manipulation. The framework uses a VLM-based Planner and an Executor, connected through Flexible Chain-of-Thought intermediate representations to bridge high-level planning and low-level action execution.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

RoboInter-Data: Large-scale human-verified dataset with dense per-frame intermediate annotations

A large-scale manipulation dataset containing over 200,000 episodes across 571 scenes with human-verified, dense per-frame annotations of nine intermediate representation categories (subtasks, primitive skills, affordances, target points, gripper/object bounding boxes, traces, contact points, and placement affordance). This dataset surpasses prior work in both scale and annotation quality.

Contribution

RoboInter-VQA: Spatial and temporal embodied VQA benchmark and training data

A curated embodied visual question answering dataset and benchmark comprising 8 spatial and 20 temporal QA categories designed to systematically evaluate and improve the embodied reasoning and grounding capabilities of vision-language models in manipulation scenarios.

Contribution

RoboInter-VLA: Flexible plan-then-execute framework with modular and end-to-end variants

A flexible plan-then-execute framework that supports multiple architectural variants (implicitly-conditioned, explicitly-conditioned, and modular) for robotic manipulation. The framework uses a VLM-based Planner and an Executor, connected through Flexible Chain-of-Thought intermediate representations to bridge high-level planning and low-level action execution.

Table of Contents