A pressure field model for fast, robust approximation of net contact force and moment between nominally rigid objects We introduce Pressure Field Contact (PFC), an approximate model for predicting the contact surface, pressure distribution, and net contact wrench between nominally rigid objects. PFC combines and generalizes two ideas: a bed of springs (an xe2x80x98elastic foundationxe2x80x99) and hydrostatic pressure. Continuous pressure fields are computed offline for the interior of each nominally rigid object. Unlike hydrostatics or elastic foundations, the pressure fields need not satisfy mechanical equilibrium conditions. When two objects nominally overlap, a contact surface is defined where the two pressure fields are equal. This static pressure is supplemented with a dissipative ratedependent pressure and friction to determine tractions on the contact surface. The contact wrench between pairs of objects is an integral of traction contributions over this surface. PFC evaluates much faster than elasticitytheory models, while showing the essential trends of force, moment, and stiffness increase with contact load. It yields continuous wrenches even for nonconvex objects and coarse meshes. The method shows promise as sufficiently fast, accurate, physical, and robust for robotics applications including motion and tactile sensor simulation, controller learning and synthesis, state estimation, and designinsimulation.

Passive Model Reduction and Switching for Fast Soft Object Simulation with Intermittent Contacts We propose a novel fast simulation framework for soft objectsrobots with intermittent contacts, whose contact areaslocations can be varying. We first perform a balanced model reduction of the fullorder FEM (finite element method) model for each contact mode with the contact forcing as the input and the shape of the objectrobot as the output. We then devise the strategy of passive model reduction and passive model switching of these reducedorder models (each with its contact mode) utilizing the techniques of our recentlyproposed passive midpoint integration (for the passivity of each reducedorder model) and simultaneous diagonalization (for the passivity of model reduction and model switching). The efficacy of the theory is then demonstrated with simulation and experimental results.