Data-driven Friction Models for Simulation and Material Fabrication

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Friction modeling is an important consideration when designing machines, controlling robots, and for many healthcare applications. Examples include constructing more comfortable prostheses, predicting the physical behaviour of objects during robotic manipulation, or understanding human-machine interactions between a surgeon and a medical robot. However, standard friction models fail to capture the rich behaviour arising from interactions between real-world surfaces. Furthermore, common models, such as Coulomb, are empirical and hence they are limited in their capability for computational design applications based on surface microgeometry and related material properties.This research project is about developing a better understanding of the physical world and simulating friction using a data-driven approach. Furthermore, an improved understanding will lead to improved models for frictional simulation that can faithfully reproduce real-world behaviour. We then plan to leverage these simulations for computational inverse design applications, where frictional surfaces may be fabricated according to specifications or tasks defined in a numerical optimization pipeline.Our project combines interdisciplinary themes and a team of experts with complementary skills in contact simulation, data-driven modeling for virtual environments, and computational fabrication. The main objectives will be achieved by three sub-projects, which are summarized below.1. Measurement and Analysis of Friction: Humans have the amazing capability to identify the frictional properties of objects simply from the sense of touch. Inspired by this, we will use scans of surface microgeometry to infer frictional behaviour between surfaces by developing data-driven friction models. We will also use robots to collect large quantities of sliding data of different objects and artificial surfaces to help with the ultimate challenge of solving the inverse modelling problem-- designing friction from micro-surface structures. Additionally, this dataset will provide an invaluable resource for validating and benchmarking physics simulation involving friction, which is of interest for many research communities.2. Geometry driven and Data-driven Models of Frictional Behaviour: Formulating novel friction models and new computational approaches will be a key component of our work. We are motivated by recent machine learning techniques for their ability to predict and regress complex, non-linear functions. Aggregate friction effects arising from micro-surface interactions represent such a function, and we therefore intend to leverage modern techniques to learn mappings from surface micro-geometry to aggregate friction behaviour. The result will be a new class of models for realistic friction simulation.3. Computational Fabrication of Materials by Inverse Friction Modeling: Although friction is a functional aspect of many physical systems, methods for designing and fabricating rough surfaces that meet user-specified functional requirements are nearly non-existent. This presents us with an interesting research opportunity to develop inverse modeling and optimization techniques that are based on our data-driven friction models that will facilitate new technologies for automatic design and fabrication of friction. This has a wide range of applications, such as creating artificial skin for gloves to better grasp, designing task-specific shoe soles (e.g., for curling or other sports), or fabricating mechanical macrostructure 'coatings' of objects without using chemicals.