DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation

arXiv:2609.24976 · cs.RO, cs.AI, cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.RO, cs.AI, cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: 22 pages. Project website: https://dextacwam.github.io/

Project page: https://dextacwam.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 24× in this paper · explained in 7 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Adaptation — 9× in this paper · explained in 1 episode
• Embeddings — 7× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Latent Space — 4× in this paper · explained in 7 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Reactive Diffusion Policy — 3× in this paper
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• VLA Models — 3× in this paper · explained in 3 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• In Conference on Robot Learning — 2× in this paper
• Multimodal Learning — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes

Sources

• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• RT-1: Robotics Transformer for Real-World Control at Scale
• Genie: Generative Interactive Environments
• OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing
• World Models
• Mastering Diverse Domains through World Models
• ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation
• Visuo-Tactile World Models
• Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization
• Spatially anchored Tactile Awareness for Robust Dexterous Manipulation
• OpenVLA: An Open-Source Vision-Language-Action Model
• Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
• Octo: An Open-Source Generalist Robot Policy
• FACT: Failure-Aware Causal Training for World-Action Models
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning
• VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs
• OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation

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