GazeVLA: Learning Human Intention for Robotic Manipulation
Overview
This paper introduces GazeVLA, a framework that leverages human gaze cues to model intentions for robotic manipulation. By capturing the synergy between high-level task semantics and low-level actions encoded in gaze, the method derives mid-level representations that facilitate cross-embodiment knowledge transfer. The approach employs a Chain-of-Thought reasoning paradigm during inference to handle long-horizon and fine-grained tasks across diverse robot embodiments. Experiments in both simulation and real-world environments demonstrate consistent outperformance over baseline methods and generalization to unseen settings, all without requiring robotic intention annotations.
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Dataset · tasks: robotic manipulation
“GazeVLA: Learning Human Intention for Robotic Manipulation”
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“man intentions to enhance manipulation, even without robotic intention annotations. To capture the synergy between intention and action, we adopt a Chain-of-Thought reasoning paradigm during inference. Our approach enables the execution of long-horizon and fine-grained tasks across diverse embodiments. Extensive experiments in both simulation and real-world demonstrate that our method consistently outperforms baselin”
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Dataset · embodiments: diverse embodiments
“man intentions to enhance manipulation, even without robotic intention annotations. To capture the synergy between intention and action, we adopt a Chain-of-Thought reasoning paradigm during inference. Our approach enables the execution of long-horizon and fine-grained tasks across diverse embodiments. Extensive experiments in both simulation and real-world demonstrate that our method consistently outperforms baselin”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalGazeVLA: Learning Human Intention for Robotic Manipulation
This paper introduces GazeVLA, a framework that leverages human gaze cues to model intentions for robotic manipulation. By capturing the synergy between high-level task semantics and low-level actions encoded in gaze, the method derives mid-level representations that facilitate cross-embodiment knowledge transfer. The approach employs a Chain-of-Thought reasoning paradigm during inference to handle long-horizon and fine-grained tasks across diverse robot embodiments. Experiments in both simulation and real-world environments demonstrate consistent outperformance over baseline methods and generalization to unseen settings, all without requiring robotic intention annotations.
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