# TactileLab

TactileLab moves tangential motion out of soft-body deformation and generates a dense tactile flow field directly from rigid-body simulation, feeding it to policies alongside contact depth images. Rigid-body tactile simulators previously offered mainly depth-like signals that could not express shear or slip, while soft-body simulators captured deformation but could not sustain large-scale reinforcement learning. On dual-arm following and dexterous in-hand rotation, policies with tactile flow converged faster, and on real hardware the depth-plus-flow policy completed the most rotations across cylindrical, round and cube-shaped objects.

TactileLab moves tangential motion out of soft-body deformation and generates a dense tactile flow field directly from rigid-body simulation, feeding it to policies alongside contact depth images. Rigid-body tactile simulators previously offered mainly depth-like signals that could not express shear or slip, while soft-body simulators captured deformation but could not sustain large-scale reinforcement learning. On dual-arm following and dexterous in-hand rotation, policies with tactile flow converged faster, and on real hardware the depth-plus-flow policy completed the most rotations across cylindrical, round and cube-shaped objects.