Modeling, Simulation and Design of Skin Deformation and Sensorization for Humans and Robots

Abstract

In this thesis, we present three di erent contributions to the computer graphics, digital fabrication and haptics communities. First, we introduce a new technique to augment parametric skeletal models with subspace soft-tissue deformations. We combine the bene ts of data-driven skeletal models, i.e., accurate replication of contact-free static deformations, with the bene ts of pure physics-based models, i.e., skin and skeletal reaction to contact and inertial motion with two-way coupling. We succeed to do so in a highly e cient manner, thanks to a careful choice of reduced model for the subspace deformation. With our method, it is easy to design expressive reduced models with e cient yet accurate force computations, without the need for training deformation examples. We demonstrate the application of our method to parametric models of human bodies, SMPL, and hands, MANO, with interactive simulations of contact with nonlinear soft-tissue deformation and skeletal response. As our second contribution, we explore the sensorization of soft tissue, exploring human perception and introducing new bio-inspired methods for both, the designing of the simulated sensors and the approximation of the human perception, transforming sensor readings into data that humans can understand. This way we try to approximate the ability of humans to sense shape, pressure and even the position of the body. Finally, we take a similar approach into a practical case and we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to user-provided soft robotic designs, which is optimized to perform well under a set of user-speci ed deformation-force pairs. The sensorized robots are able to reconstruct their full deformation state, under interaction forces. We cast our sensor design as a subselection problem, selecting a minimal set of sensors from a large set of fabricable ones, which minimizes the error when sensing speci ed deformation-force pairs. Unique to our approach is the use of an analytical gradient of our reconstruction performance measure with respect to selection variables with the use of silicone soft robots and we design a sensorization and reconstruction pipeline that allow placeing sensors in the silicone and use them to retrieve the position of the robot at any point in time.