ZeroTouch: Tactile-Supervised Visual Contact Estimation for Contact-Rich Manipulation

arXiv:2609.21726 · cs.RO · Submitted 2026-09-18 · Read on arXiv

cs.RO

Submitted: 2026-09-18

Updated: 2026-09-18

Terminology

• Distribution Shift — 5× in this paper · explained in 5 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations
• FELT: Generating Tactile Signals from Vision for Visuo-Tactile Manipulation
• Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation
• Feel the Force: Contact-Driven Learning from Humans
• Learning Force-Regulated Robotic Manipulation with a Low-Cost Tactile-Force-Controlled Gripper

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving