Spark-0: Scaling Spatially Aligned Tactile Pretraining for Robot Dexterous Manip
Overview
Spark-0 is a vision-tactile-language-action model that leverages tactile data to enhance robot dexterous manipulation. The model uses a 1,000-hour tactile dataset, HumanTouch, and combines it with visual data and simulation for pretraining. It achieves the highest mean success rate on SparkArena and improves success rates on both simulated and real-world tasks when tactile data is included.
From arXiv Robotics — research abstracts
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Reported quantity · Reported duration: 1000 hours · Basis not reported
“h, a 1,000-hour dataset of full-hand tactile human manipulation, and pretrain Spark-0 on it together with egocentric video, robot data, and simulation. We also release SparkArena, which pairs vision-only simulated tasks with tactile-conditioned real-robot tasks on one dexterous embodiment. Spark-0 achieves the highest mean success rate on both SparkArena tracks and ranks first on all eight aggregate metrics of the Eg”
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- 2026-10-30T22:50:20.142Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T22:50:02.544Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalSpark-0: Scaling Spatially Aligned Tactile Pretraining for Robot Dexterous Manipulation
Spark-0 is a vision-tactile-language-action model that leverages tactile data to enhance robot dexterous manipulation. The model uses a 1,000-hour tactile dataset, HumanTouch, and combines it with visual data and simulation for pretraining. It achieves the highest mean success rate on SparkArena and improves success rates on both simulated and real-world tasks when tactile data is included.
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