Dexterous Tactile World Model

arXiv:2609.34286 · cs.CV, cs.LG, cs.RO · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.LG, cs.RO

Submitted: 2026-09-28

Updated: 2026-09-28

Project page: https://adonis-galaxy.github.io/dtwm-project-page/1

Terminology

• World Model — 45× in this paper · explained in 7 episodes
• Transformer — 19× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Dexterous Tactile World Model — 4× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• International Conference on Computer Vision — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Association for the Advancement of Artificial Intelligence — 1× in this paper
• Decoupling — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• In European Conference on Computer Vision — 1× in this paper
• International Conference on Robotics and Automation — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes

Sources

• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• Visuo-Tactile World Models
• ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation
• OpenLongTail: Generative Scaling of Long-Tail Driving Data
• Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning
• FeelWorld: Visuo-Tactile World Model for Hierarchical Contact Prediction and Planning
• Cosmos World Foundation Model Platform for Physical AI
• Decoupling Vision, Language, and Action for Efficient Multi-Task Robot Policies
• Artificial Foveated Perception for Mitigating Shortcut Learning in Robotic Foundation Models
• Wan: Open and Advanced Large-Scale Video Generative Models
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
• Coffee: Controllable Diffusion Fine-tuning
• ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation
• TouchAnything: A Dataset and Framework for Bimanual Tactile Estimation from Egocentric Video
• TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous Manipulation

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