Arjun Sriram Lakshmikpathy

Humans use their hands to effortlessly manipulate objects of arbitrarily complex geometries and physical properties every day; however, adapting these behaviors to dexterous robots and virtual characters is an extremely difficult task. Understanding the ways in which humans exploit contact to perform these manipulations has the potential to greatly advance progress towards this goal.

Unsurprisingly, research efforts have analyzed contact in the context of dexterous manipulation for decades. We now have numerous metrics for evaluating the quality of dexterous grasps in terms of contacts, sophisticated models of contact states, efficient means of computing contact in physical simulation, and countless strategies that exploit contact correspondences between hands and objects to synthesize grasps and manipulations. But the majority of existing works fundamentally characterize contact in the same way: as points, lines, or planes of interaction between surfaces.

Although this simplification has proven reasonable for a number of applications, contact in the real world is much more complicated. Instead, real bodies interface with one another via areas of contact which greatly vary with the geometries of the contacting surfaces. If we wish to improve our understanding and model the complexities of manipulations as they actually occur, then we must progress beyond such simplifying assumptions and deal with the messy nature of reality. Yet surprisingly, there have been relatively few research efforts in this direction.

This thesis aims to change the current contact modeling narrative by presenting foundational frameworks and algorithms for the modeling, capture, mutation, and exploitation of contact areas. Our intention is to establish the foundations necessary to elevate contact regions to first-class primitives and demonstrate the inherent value they provide across a range of practical applications in both dexterous manipulation and adjacent domains.

First, we introduce three novel models of contact areas alongside a collection of operations supported by each model fundamentally designed to run on real discrete geometries rather than primitive shapes. Next, using area-based contact primitives, we introduce: a set of intuitive artist tools for digitally drafting high quality grasps, a kinematic motion retargeting pipeline for dexterous manipulations, a contact-driven control framework for dexterous robot hands in physical simulation, and two practical extensions of our contributions to different domains. We then shift our focus to the real world by introducing two approaches for capturing and reconstructing contact regions during human-object and human-human interactions. Finally, we present an end-to-end system architecture framework for constructing fully functional robot systems from contact-rich human demonstrations. The contributions in this thesis are not intended to be the last words, but rather important first steps designed to promote future research efforts in contact area modeling and utilization.