GAP pre-trains the spatial adapter on a lightweight simulated proxy task with free object masks to generate repeatable geometric keypoints, yielding higher success rates than baselines in low-data robotic manipulation on RoboMimic and ManiSkill.
Deep spatial autoencoders for visuomotor learning,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MSACT improves localization stability and task success rates in limited-data bimanual manipulation by extracting stable 2D attention points and aligning predicted attention sequences across frames without keypoint labels.
citing papers explorer
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GAP: Geometric Anchor Pre-training for Data-Efficient Visuomotor Learning of Manipulation Tasks
GAP pre-trains the spatial adapter on a lightweight simulated proxy task with free object masks to generate repeatable geometric keypoints, yielding higher success rates than baselines in low-data robotic manipulation on RoboMimic and ManiSkill.
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MSACT: Multistage Spatial Alignment for Stable Low-Latency Fine Manipulation
MSACT improves localization stability and task success rates in limited-data bimanual manipulation by extracting stable 2D attention points and aligning predicted attention sequences across frames without keypoint labels.