OpenNovelty.org

RoboOmni: Proactive Robot Manipulation in Omni-modal Context

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
SpeechRobotic ManipulationOmni-Modal LLMsProactive Intention Recognition
Abstract:

Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision–Language–Action (VLA) models for robotic manipulation. Although effective in many scenarios, current approaches largely rely on explicit instructions, whereas in real-world interactions, humans rarely issue instructions directly. Effective collaboration requires robots to infer user intentions proactively. In this work, we introduce cross-modal contextual instructions, a new setting where intent is derived from spoken dialogue, environmental sounds, and visual cues rather than explicit commands. To address this new setting, we present RoboOmni, a Perceiver-Thinker-Talker-Executor framework based on end-to-end omni-modal LLMs that unifies intention recognition, interaction confirmation, and action execution. RoboOmni fuses auditory and visual signals spatiotemporally for robust intention recognition, while supporting direct speech interaction. To address the absence of training data for proactive intention recognition in robotic manipulation, we build OmniAction, comprising 140k episodes, 5k+ speakers, 2.4k event sounds, 640 backgrounds, and six contextual instruction types. Experiments in simulation and real-world settings show that RoboOmni surpasses text- and ASR-based baselines in success rate, inference speed, intention recognition, and proactive assistance. All datasets, code, and real-world demonstration videos will be released publicly.

Disclaimer
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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces a framework for proactive robot manipulation that infers user intentions from cross-modal contextual cues—spoken dialogue, environmental sounds, and visual signals—rather than explicit commands. Within the taxonomy, it occupies the 'Omni-Modal Intention Recognition from Contextual Instructions' leaf under 'Proactive Intention Recognition and Human-Robot Collaboration'. Notably, this leaf contains only the original paper itself, indicating a sparse research direction. The broader parent branch includes four leaves with a total of four sibling papers, suggesting that proactive intention recognition remains an emerging area compared to more crowded branches like 'Cross-Modal Perception and Fusion for Manipulation' (five papers across three leaves).

The taxonomy reveals that neighboring work primarily focuses on vision-language collaboration (e.g., 'Vision-Language Collaboration for Proactive Task Assistance') or multimodal subtask recognition without audio integration. The 'Action Anticipation from Multimodal Context' branch addresses future action prediction but excludes proactive manipulation execution, while 'Cross-Modal Perception and Fusion for Manipulation' emphasizes sensor fusion for reactive control rather than intention inference. The scope note for the paper's leaf explicitly excludes methods using only vision-language without audio or lacking proactive intention inference, positioning this work at the intersection of omni-modal sensing and anticipatory collaboration—a boundary less explored in the surveyed literature.

Among thirty candidates examined across three contributions, none were found to clearly refute the paper's claims. The 'cross-modal contextual instructions setting' examined ten candidates with zero refutable overlaps, as did the 'RoboOmni framework' and 'OmniAction dataset' contributions. This suggests that within the limited search scope, the specific combination of audio-visual-dialogue fusion for proactive manipulation and the associated dataset appear relatively novel. However, the analysis is constrained to top-K semantic matches and does not cover exhaustive domain-specific venues or recent preprints, leaving open the possibility of related work outside the examined set.

Based on the limited search scope of thirty candidates, the work appears to occupy a sparsely populated research direction, with no sibling papers in its taxonomy leaf and no clear prior work refuting its core contributions. The taxonomy structure indicates that while proactive intention recognition is an active area, the specific omni-modal approach combining audio, vision, and dialogue for manipulation remains underexplored. Acknowledging the search limitations, the analysis covers top semantic matches but may not capture all relevant domain-specific or concurrent efforts.

Taxonomy

Core-task Taxonomy Papers
17
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: proactive robot manipulation from cross-modal contextual instructions. The field addresses how robots can anticipate and execute manipulation tasks by integrating diverse sensory modalities—vision, language, touch, and proprioception—with contextual cues about human intent. The taxonomy reveals five main branches: Cross-Modal Perception and Fusion for Manipulation focuses on combining heterogeneous sensor streams to build unified representations for action (e.g., ViTacFormer[1], Multimodal Perception Fusion[9]); Proactive Intention Recognition and Human-Robot Collaboration emphasizes inferring user goals before explicit commands (e.g., Proactive Robot Assistants[8], Summarize Past Predict[3]); Action Anticipation from Multimodal Context explores predicting future human or robot actions from ego-centric or third-person observations (e.g., Ego-centric Predictive Hand[11], Text Input Anticipation[4]); Reactive Instruction Execution and Visual Foresight deals with executing given instructions while forecasting outcomes (e.g., Vision-Language Reinforcement[13], Generative Visual Foresight[14]); and Multimodal Interface Design for Supervisory Control examines how operators interact with robots through combined modalities (e.g., Multi-modal Supervisory Interfaces[15], Multimodal Commands Feedback[16]). A central tension across these branches is the trade-off between reactive execution—where the robot waits for explicit instructions—and proactive anticipation, where it infers intent from partial or implicit cues. Works like Proactive Visuo-Lingual[2] and Summarize Past Predict[3] highlight how summarizing past interactions can enable early action, while Cross-modal Contrastive Distillation[5] and Interactive Agent Foundation[6] show that large-scale pretraining on diverse modalities improves generalization. RoboOmni[0] sits squarely within the Proactive Intention Recognition and Human-Robot Collaboration branch, specifically targeting omni-modal intention recognition from contextual instructions. Compared to Proactive Visuo-Lingual[2], which primarily fuses vision and language, RoboOmni[0] extends the modality palette further, and relative to Summarize Past Predict[3], it emphasizes real-time contextual fusion rather than retrospective summarization. This positioning underscores an emerging direction: leveraging richer sensory contexts to enable robots to act before users finish articulating their needs.

Claimed Contributions

Cross-modal contextual instructions setting

The authors define a novel problem setting where robots must infer user intent from implicit multimodal cues—including speech, environmental sounds, and visual observations—instead of relying on explicit commands. This setting emphasizes proactive reasoning and interaction confirmation.

10 retrieved papers
RoboOmni framework

RoboOmni is an end-to-end omni-modal framework that integrates perception, reasoning, speech interaction, and action execution. It spatiotemporally fuses audio (speech and environmental sounds) and vision to recognize and confirm user intent before executing manipulation actions.

10 retrieved papers
OmniAction dataset

The authors construct OmniAction, a large-scale dataset designed for training and evaluating proactive intention recognition in robotic manipulation. It includes diverse speakers, environmental sounds, and six categories of contextual instructions, plus a simulation benchmark.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Cross-modal contextual instructions setting

The authors define a novel problem setting where robots must infer user intent from implicit multimodal cues—including speech, environmental sounds, and visual observations—instead of relying on explicit commands. This setting emphasizes proactive reasoning and interaction confirmation.

Contribution

RoboOmni framework

RoboOmni is an end-to-end omni-modal framework that integrates perception, reasoning, speech interaction, and action execution. It spatiotemporally fuses audio (speech and environmental sounds) and vision to recognize and confirm user intent before executing manipulation actions.

Contribution

OmniAction dataset

The authors construct OmniAction, a large-scale dataset designed for training and evaluating proactive intention recognition in robotic manipulation. It includes diverse speakers, environmental sounds, and six categories of contextual instructions, plus a simulation benchmark.

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