Research Models & Releases·arXiv cs.CL·4d ago

IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation

IntentVLA addresses a fundamental challenge in robot learning: multimodal imitation data where identical observations lead to different actions depending on unobserved human intent or task phase. By conditioning action generation on a learned compact representation of recent visual history rather than single-frame observations, the framework reduces execution instability from conflicting intent switches during replanning. The accompanying AliasBench benchmark quantifies this ambiguity problem across 12 manipulation tasks, establishing a new evaluation standard for intent-aware robot policies and advancing practical deployment of vision-language models in embodied AI.

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Explainer

The core insight is that robot imitation data suffers from fundamental ambiguity: the same visual frame can legitimately map to different actions depending on task phase or human intent that cameras never see. IntentVLA sidesteps this by conditioning on short temporal windows rather than single frames, reducing the aliasing problem rather than solving it outright.

This connects directly to the Clinical World Model work from earlier today (Agentifying Patient Dynamics). Both papers identify the same architectural failure: models trained on static observations or parametric knowledge alone fail when outcomes depend on hidden state or prior context. IntentVLA uses visual history as a proxy for intent; SepsisAgent uses an environment simulator as a proxy for patient physiology. The pattern is grounding learned representations in temporal or causal structure rather than treating each decision as independent. AliasBench also mirrors the benchmark-building approach in Video2GUI, which automated trajectory extraction to scale training data. Here the contribution is quantifying a specific failure mode rather than scaling data collection.

If IntentVLA's performance gains on AliasBench hold when tested on out-of-distribution task sequences (new human operators, novel object categories), that confirms the temporal conditioning actually captures generalizable intent patterns. If performance degrades significantly on held-out intent types, the method is overfitting to the training distribution's ambiguity structure rather than solving aliasing.

Coverage we drew on

Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model · arXiv cs.CL

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MentionsIntentVLA · AliasBench · RoboTwin2 · VLA

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