A task-aware policy learned via reinforcement learning allocates high-resolution pixels on dual-stream sensors in real time, outperforming fixed or non-predictive baselines under tight pixel budgets in both simulation and 200 MP hardware tests.
Look, focus, act: Efficient and robust robot learning via human gaze and foveated vision transform- ers
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GazeVLA pretrains on large human egocentric datasets to capture gaze-based intention, then finetunes on limited robot data with chain-of-thought reasoning to achieve better robotic manipulation performance than baselines.
Across 20 Atari games, removing peripheral visual input drops human-action prediction accuracy by median 35–44%, far more than removing gaze maps (~2%) or past states (1.5–15%).
HoMMI learns whole-body mobile manipulation policies from robot-free human demonstrations by augmenting UMI with egocentric sensing and bridging the embodiment gap through an agnostic visual representation, relaxed head actions, and a whole-body controller.
citing papers explorer
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Policy-based Foveated Imaging and Perception
A task-aware policy learned via reinforcement learning allocates high-resolution pixels on dual-stream sensors in real time, outperforming fixed or non-predictive baselines under tight pixel budgets in both simulation and 200 MP hardware tests.
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GazeVLA: Learning Human Intention for Robotic Manipulation
GazeVLA pretrains on large human egocentric datasets to capture gaze-based intention, then finetunes on limited robot data with chain-of-thought reasoning to achieve better robotic manipulation performance than baselines.
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Estimating Central, Peripheral, and Temporal Visual Contributions to Human Decision Making in Atari Games
Across 20 Atari games, removing peripheral visual input drops human-action prediction accuracy by median 35–44%, far more than removing gaze maps (~2%) or past states (1.5–15%).
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HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations
HoMMI learns whole-body mobile manipulation policies from robot-free human demonstrations by augmenting UMI with egocentric sensing and bridging the embodiment gap through an agnostic visual representation, relaxed head actions, and a whole-body controller.