HapticWAM: Distilling Imagined Touch into a World-Action Model without Inference-Time Tactile Sensing

arXiv:2609.23888 · cs.RO · Submitted 2026-09-20 · Read on arXiv

cs.RO

Submitted: 2026-09-20

Updated: 2026-09-20

Project page: https://advanced-robotic-manipulation.github.io/websites/hapticwam/Abstract

Terminology

• Distillation — 21× in this paper · explained in 2 episodes
• World Model — 5× in this paper · explained in 7 episodes
• Diffusion Policy — 4× in this paper
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Anticipatory Contact Coupling — 3× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Aperture — 2× in this paper · explained in 1 episode
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Action Groups — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Diffusion Policy (DP) — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Heterogeneous Haptic Tokenizer — 1× in this paper
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• On-Policy Distillation — 1× in this paper · explained in 3 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization
• $N_0$-VTLA: Scaling Vision-Tactile-Language-Action Model with Latent Tactile Tokens
• HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing
• Cosmos World Foundation Model Platform for Physical AI
• World Action Models are Zero-shot Policies
• World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems
• Towards Predictive, Aligned, and Scalable Robot Learning
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
• Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention
• $N_0$-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation
• Being-H0.7: A Latent World-Action Model from Egocentric Videos
• VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation
• TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation
• FlowTouch: View-Invariant Visuo-Tactile Prediction

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