arXiv Computer Vision
DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation
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DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination
DeCAL is a vision‑language‑action model designed for dexterous manipulation that incorporates tactile sensing through adaptive visuo‑tactile fusion and latent co‑imagination. It uses a Mixture‑of‑Transformers architecture with specialized experts for understanding, imagination, and action, enabling efficient information flow and dynamic regulation of tactile inputs. Experiments show DeCAL achieves state‑of‑the‑art performance, with a 71% average success rate and 83.4% progress success rate, and generalizes well to unseen scenarios.