IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation

Hugging Face Daily Papers Papers
robot-manipulation imitation-learning visual-language-action intent-modeling partial-observability benchmark

Summary

IntentVLA is a history-conditioned visual-language-action framework that improves robot imitation learning stability by encoding short-horizon intents from visual observations, addressing challenges from partial observability and ambiguous observations. It also introduces AliasBench, an ambiguity-aware benchmark for evaluating such methods.

Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with different short-horizon intents, task phases, or recent context. Existing frame-conditioned VLA policies infer each chunk from the current observation and instruction alone, so under partial observability they may resample different intents across adjacent replanning steps, leading to inter-chunk conflict and unstable execution. We introduce IntentVLA, a history-conditioned VLA framework that encodes recent visual observations into a compact short-horizon intent representation and uses it to condition chunk generation. We further introduce AliasBench, a 12-task ambiguity-aware benchmark on RoboTwin2 with matched training data and evaluation environments that isolate short-horizon observation aliasing. Across AliasBench, SimplerEnv, LIBERO, and RoboCasa, IntentVLA improves rollout stability and outperforms strong VLA baselines
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Abstract

IntentVLA is a history-conditioned visual-language action framework that improves robot imitation learning stability by encoding short-horizon intents from visual observations, addressing challenges from partial observability and ambiguous observations.

Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with differentshort-horizon intents, task phases, or recent context. Existingframe-conditionedVLA policies infer each chunk from the current observation and instruction alone, so underpartial observabilitythey may resample different intents across adjacent replanning steps, leading to inter-chunk conflict and unstable execution. We introduce IntentVLA, ahistory-conditionedVLA framework that encodes recent visual observations into a compact short-horizonintent representationand uses it to condition chunk generation. We further introduceAliasBench, a 12-taskambiguity-aware benchmarkonRoboTwin2with matched training data and evaluation environments that isolate short-horizon observation aliasing. AcrossAliasBench, SimplerEnv, LIBERO, and RoboCasa, IntentVLA improvesrollout stabilityand outperforms strong VLA baselines

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