Reliable robotic manipulation requires both semantic understanding and precise control in three-dimensional space. However, vision-language-action (VLA) foundations largely inherit visual representati
SLA is a spatial-language-action model that continually pre-trains and post-trains a pretrained VLA policy with spatial alignment to a frozen 3D perception model, using feature-dependent gates and probabilistic state dropout to improve spatial understanding across diverse robot data while…
Reliable robotic manipulation requires both semantic understanding and precise control in three-dimensional space. However, vision-language-action (VLA) foundations largely inherit visual representations from 2D vision-language pre-training, while spatial supervision is often introduced only during task-specific adaptation. Developing spatial understanding as a transferable foundation-model capability therefore calls for learning from broader robot experience. We present SLA, a Spatial-Language-Action Model that scales implicit spatial alignment to continual pre-training and maintains it during downstream post-training. Initialized from a pretrained pi05 policy, SLA jointly optimizes…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
SLA: Scaling Spatial-Language-Action Model via Continual Pre-training was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 29 checks on 3 different days. Its best position was #1 of 30, reached on Nov 7, 07:28 UTC. It is #3 right now, holding steady at #3 over the last 6 checks.
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Yes. As of Nov 8, 17:28 UTC it is #3, holding steady at #3 over the last 6 checks.
#1 on Hugging Face · Daily papers, on Nov 7, 07:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 6, 2026 and has been on the list on 3 days (29 checks) since.