ME-Dex 1.0: Bringing Heterogeneous Tactile Sensing into World Action Modeling

arXiv:2609.21449 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-21

Code: https://github.com/MachEmbodied/ME-Dex-1.0

Project page: https://machembodied.com/ME-Dex/ME-Dex1.0.html

Terminology

• Transformer — 20× in this paper · explained in 2 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• World Model — 9× in this paper · explained in 7 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Latent Space — 8× in this paper · explained in 7 episodes
• Flow Matching — 6× in this paper · explained in 8 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Foundation Model — 2× in this paper · explained in 3 episodes
• IEEE International Conference on Robotics and Automation — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Action Decoder — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Joint Attention — 1× in this paper · explained in 2 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
• Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets
• World Action Models are Zero-shot Policies
• GigaWorld-Policy: An Efficient Action-Centered World--Action Model
• ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation
• Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization
• OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation
• VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation
• DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
• ManiFeel: Benchmarking and Understanding Visuotactile Manipulation Policy Learning
• $\tau_0$-WM: A Unified Video-Action World Model for Robotic Manipulation
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• Motubrain: An Advanced World Action Model for Robot Control
• T-Rex: Tactile-Reactive Dexterous Manipulation
• Motus2: A Self-Evolving General World Model for Dexterous Manipulation
• Visuo-Tactile World Models
• TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous Manipulation
• ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation
• Wan: Open and Advanced Large-Scale Video Generative Models

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