Incorporating accurate physics-based simulation into interactive design tools is challenging. However, adding the physics accurately becomes crucial to several emerging technologies. For example, in virtual augmented reality (VR AR) videos, the faithful reproduction of surrounding audios is required to bring the immersion to the next level. Similarly, as personal fabrication is made possible with accessible 3D printers, more intuitive tools that respect the physical constraints can help artists to prototype designs. One main hurdle is the sheer amount of computation complexity to accurately reproduce the real-world phenomena through physics-based simulation. In my thesis research, I develop interactive tools that implement efficient physics-based simulation algorithms for automatic optimization and intuitive user interaction.
To design custom-shaped geometries with more complex physical phenomena, offline computational optimization is usually used in the design process. Digital Mechanical Metamaterials proposes to embody mechanical movements into 3D printed objects using a modular method @cite_6 . Each of the modular cells is specially designed such that they can pass digital information when connected together. Acoustruments introduced a passive acoustic-based mechanisms for interactive controls on smartphones @cite_2 . Through carefully designed tube geometries and materials, an expansive dataset of design primitives is generated for easy construction. To reproduce physical haptic interaction during fabrication, HapticPrint explores and builds a library of various patterns to different types of compliance @cite_9 . While this type of research is a promising direction, I would like to investigate how to better combine computational design with intuitive tools for the users.
@cite_9 @cite_6 @cite_2
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In this paper, we explore how to embody mechanical computation into 3D printed objects, i.e., without electronic sensors, actuators, or controllers typically used for this purpose. A key benefit of our approach is that the resulting objects can be 3D printed in one piece and thus do not require assembly. We are building on 3D printed cell structures, also known as metamaterials. We introduce a new type of cell that propagates a digital mechanical signal using an embedded bistable spring. When triggered, the embedded spring discharges and the resulting impulse triggers one or more neighboring cells, resulting in signal propagation. We extend this basic mechanism to implement simple logic functions. We demonstrate interactive objects based on this concept, such as a combination lock. We present a custom editor that allows users to model 3D objects, route signals, simulate signal flow, and synthesize cell patterns. We present an interactive design system to create functional mechanical objects. Our computational approach allows novice users to retarget an existing mechanical template to a user-specified input shape. Our proposed representation for a mechanical template encodes a parameterized mechanism, mechanical constraints that ensure a physically valid configuration, spatial relationships of mechanical parts to the user-provided shape, and functional constraints that specify an intended functionality. We provide an intuitive interface and optimization-in-the-loop approach for finding a valid configuration of the mechanism and the shape to ensure that higher-level functional goals are met. Our algorithm interactively optimizes the mechanism while the user manipulates the placement of mechanical components and the shape. Our system allows users to efficiently explore various design choices and to synthesize customized mechanical objects that can be fabricated with rapid prototyping technologies. We demonstrate the efficacy of our approach by retargeting various mechanical templates to different shapes and fabricating the resulting functional mechanical objects. We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry. We develop a computationally efficient control policy for active perception that incorporates explicit models of sensing and mobility to build 3D maps with ground and aerial robots. Like previous work, our policy maximizes an information-theoretic objective function between the discrete occupancy belief distribution (e.g., voxel grid) and future measurements that can be made by mobile sensors. However, our work is unique in three ways. First, we show that by using Cauchy-Schwarz Quadratic Mutual Information (CSQMI), we get significant gains in efficiency. Second, while most previous methods adopt a myopic, gradient-following approach that yields poor convergence properties, our algorithm searches over a set of paths and is less susceptible to local minima. In doing so, we explicitly incorporate models of sensors, and model the dependence (and independence) of measurements over multiple time steps in a path. Third, because we consider models of sensing and mobility, our method naturally applies to both ground and aerial vehicles. The paper describes the basic models, the problem formulation and the algorithm, and demonstrates applications via simulation and experimentation.