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原文摘要

Robotic simulators that represent shear within tactile sensing are becoming increasingly important for contact-rich manipulation. However, simulating contact shear with high-fidelity deformable or soft-body simulation is computationally demanding, limiting scalability to large-scale reinforcement learning. Here we introduce \textit{TactileLab}, a scalable multimodal tactile simulation framework built on IsaacLab, with a particular focus on a new optical-flow-based tactile representation termed \textit{simulated tactile flow}. Instead of relying on soft-body physics to model shear deformation, TactileLab uses rigid-body simulation to efficiently estimate dense tangential motion at the contact interface, producing tactile flow fields that encode shear-relevant contact dynamics. This flow field complements geometric features from a contact depth field. We further introduce a novel policy architecture for jointly processing long-horizon proprioceptive signals and rich tactile observations. Across multiple contact-rich tasks, policies trained using simulated tactile flow learn efficiently at scale and exhibit emergent contact-aware behaviours. To facilitate sim-to-real policy deployment, we then propose a novel real-to-sim tactile translation method that maps real tactile image streams to simulated tactile flow and depth fields, together with a new tactile data collection method, and validate it across diverse tactile sensor designs. Using this pipeline, we demonstrate real-world deployment on tasks encompassing dual-arm and dexterous in-hand manipulation. Overall, TactileLab provides an end-to-end sim-to-real framework for scalable tactile policy learning, enabling diverse shear-sensitive manipulation across heterogeneous tactile robotic systems.