UCLA

Although robotic locomotion and manipulation have shown some remarkable progress in the real world, the current locomotion and manipulation algorithms are inefficient in performance. They often only work for relatively simple tasks such as walking and running for locomotion and pick-and-place in structured environments (e.g., factory) for manipulation. In contrast, humans can perform quite dexterous tasks through contact as contacts provide additional dexterity to interact with environments. Hence, understanding the underlying contact mechanics plays a key role in designing contact-aware planners, controllers, and estimators for locomotion and manipulation.

However, design for planners, controllers, and estimators is extremely challenging. First, the number of contact states such as making and breaking contact with environments increases dramatically as the number of contacts increases. Thus, the underlying contact dynamics become large-scale non-smooth dynamics. As a result, optimization solvers have difficulties converging due to the non-convexity of the optimization problem.

Second, it is desirable that a robot should be able to interact in unknown environments during operation, leading to generalizable locomotion and manipulation. However, robust planning with frictional interaction with uncertain physical properties is very tough as the robot might cause undesired unexpected contact events. As a result, a robot might not be able to complete its desired task.

Third, once uncertainty is quite large, it is indispensable for closed-loop controllers to stabilize locomotion and manipulation. However, the design of manipulation is quite challenging as most manipulation systems are underactuated and unobservable with potential changes in contact states and modes.

In this dissertation, we present a methodology for contact-rich locomotion and planning using trajectory optimization. We first show that the planner using graph-search planners with trajectory optimization can be beneficial for decreasing the computation complexity. Second, we describe our contact-implicit trajectory optimization for planning of multi-limbed systems for running and climbing. We use decomposition-based optimization techniques to efficiently design a trajectory for a robot subject to various complicated contact constraints such as mixed-integer constraints. Then, we present our robust and stochastic trajectory optimization algorithms for multi-contact systems. We show that our chance-constrained optimization is applicable for planning multi-limbed robots. We also propose covariance steering algorithm for contact-rich systems using a particle filter to approximate a distribution of underlying contact dynamics. Our covariance steering is able to regulate robots' states and contact states simultaneously with probabilistic guarantees. Furthermore, utilizing the underlying structure of contact-rich manipulation, we present robust bilevel trajectory optimization for pivoting manipulation under uncertain physical parameters such as friction coefficients. Our proposed framework is able to design optimal control sequences while improving the worst-case stability margin along the manipulation. Finally, we present our closed-loop controller framework for tool manipulation using visuo-tactile feedback. Our approach enables the robot to achieve tool manipulation under unexpected contact events in closed-loop control fashion with no visual feedback for partially unknown objects.

The perspectives gained from this dissertation provide better insight into developing a contact-rich planning, estimation, and control framework for dexterous locomotion and manipulation in highly unstructured environments.

Resonant interactions between energetic radiation belt electrons and equatorially-generated whistler-mode waves are widely studied because they yield either electron acceleration or precipitation -- where electrons are scattered and lost into the Earth's atmosphere -- both of which are fundamental to space weather forecasting, which is an increasingly relevant challenge as society scales up its reliance on space technologies. This dissertation investigates the mechanisms that govern the effectiveness of electron losses from Earth's radiation belts driven by whistler-mode waves using novel electron precipitation measurements from the ELFIN CubeSats. A culmination of innovative engineering efforts and a refactored satellite operations program has allowed ELFIN to obtain over 12,500 high-quality, low-altitude electron measurements of the radiation belts. These measurements are uniquely capable of resolving the bounce loss cone, allowing us to probe the physics that drive electron precipitation in great detail. We first present a test particle simulation that directly compares ELFIN-measured electron precipitation with equatorial electron and wave measurements by the THEMIS and MMS spacecraft during magnetic conjunctions, confirming the importance of mid-high latitude wave-power. Next, we demonstrate that test particle simulations combined with an empirical wave amplitude model adequately approximate statistical ELFIN observations at the dawn, day, and dusk MLT sectors, but they significantly underestimate relativistic (>500$keV) electron losses on the nightside. To resolve this discrepancy, we additionally use quasi-linear diffusion simulation methods to find that considering wave obliquity, wave frequency, and plasma density together are required to recover the energetic portion (>100 keV) of precipitating electron spectra without overestimating the loss contributions from the quasi-linear regime (~100 keV). We conclude by presenting the ranges of wave and plasma characteristics necessary for the incorporation of accurately modeled electron loss rates into modern radiation belt models. This unlocks the potential to remotely sense equatorial wave properties using electron precipitation measurements, but also calls for future \textit{in situ} satellite experiments to more deeply understand the interconnected role of energetic electron losses in atmospheric, ionospheric, and magnetospheric dynamics.

Organic semiconductors built on π-conjugated structures have attracted significant attention due to their distinctive optical, electronic, and mechanical properties. These characteristics position them as ideal materials for various electronic devices. Notably, their strong light absorption and efficient charge transport capabilities mark organic semiconductors as promising solutions for addressing the global energy crisis through solar energy conversion. However, precise control of “soft” nanostructures formed by the noncovalently aggregated organic semiconductors for achieving desired optoelectronic properties is challenging, compared to covalently or ionically inorganic semiconductors with rigid architectures. In Chapter 1, I will introduce basic structures and properties of organic semiconductors, especially on their molecular packing behavior. The structure-property relationship of organic semiconductors will be discussed in terms of device applications, such as the development of near-infrared donor and acceptor materials for organic photovoltaics. From molecular designs to device applications, in the following chapters, I will introduce the chemistry of several organic semiconductor aggregates constructed from twisted and nonplanar π-systems and their performances in various solar energy fields, such as photocatalytic hydrogen reaction, organic photovoltaics and perovskite solar cells. In Chapter 2, I will show the self-assembly of noncovalent π-stacked organic frameworks that shows a higher activity for photocatalytic hydrogen evolution. I will first introduce the background of noncovalent π-stacked organic frameworks, which are a subclass of porous materials that consist of crystalline networks formed by self-assembly of organic building blocks through π-π interactions. π-stacked organic frameworks based on spirofluorene as central units and 3-(dicyanomethylidene)indan-1-one as end groups demonstrate strong visible light absorption from 500 nm to 700 nm and high surface area (248 m2 g–1) with 1.8 nm hydrophilic micropores, rendering them well-suited for applications in photocatalysis. The fabricated π-stacked organic frameworks nanoparticles exhibit hydrogen evolution rate up to 152 mmol h-1g-1 at room temperature and 618 mmol h-1g-1 at 70 °C. Cryo-transmission electron microscopy further reveal the native morphology of these nanoparticles and the cocatalyst Pt loading status on them. In Chapter 3, I will discuss the singlet fission property of pentacene polymer and its application in organic photovoltaics. Singlet fission is a process that converts one singlet exciton into two triplet excitons while conserving spin. This exciton multiplication process has the potential to overcome the Shockley-Queisser limit of solar power conversion efficiency. Pentacene with high mobility has proved to be ideal organic semiconductors for singlet fission organic photovoltaics. Nevertheless, the cost-intensive vacuum deposition process and the propensity of molecular aggregation in the solid state to prematurely quench triplet excitons pose challenges for their application in photovoltaics. To address these issues, a pentacene polymer is engineered with pentacene units arranged orthogonally to the polymer backbone. This design facilitates the use of pentacene-based materials in organic photovoltaics as donor materials through solution processing. Rapid conversion of photoexcited singlets into triplet pairs, occurring on a picosecond time scale (495 ps) and further dissociate into two “free” triplet excitons in 9.8 µs are observed in the pentacene polymer via transient absorption spectroscopy. The resulting photovoltaic devices based on pentacene polymer and nonfullerene acceptors demonstrate 1.92% power conversion efficiency. In Chapter 4, I will focus on a helicene-based organic semiconductor and its application as electron transport layer in inverted perovskite solar cells. Electron transport layer materials based on fullerene tend to form large clusters and undergo dimerization when exposed to light, leading to a deterioration in electron transport capability and device degradation. The nonplanar geometry of helicenes proves effective in preventing such aggregation issues, thereby enhancing device stability. We have successfully synthesized a small-molecule n-type organic semiconductor utilizing [6]helicene. This compound was employed as the electron transport layer, n-doped by organic amines, in an inverted perovskite solar cell, achieving an impressive power conversion efficiency of over 16%.

The challenge of efficiently and plausibly simulating deformable solids and fluids remains significant in the domains of Computer Graphics and Scientific Computing. This dissertation presents an in-depth exploration of physics-based simulation, with an emphasis on the Material Point Method (MPM) --- a dominant technique in this arena. Our research aims to extend the capabilities of MPM, focusing on enhancing its performance, scalability, range of applications, and integration with emerging AI technologies. We first summarize our development of optimized MPM leveraging GPU architectures. This advancement accelerates scenarios involving hundreds of millions of particles in multi-GPU computational environments. Furthermore, the thesis introduces a device-agnostic and distributed MPM framework. This system is adept at dynamically allocating workloads across multiple computing ranks, thus enabling simulations at unprecedented particle-count scales. Additionally, the dissertation examines the application of physics-based simulation, specifically MPM, in real-time contexts. It also integrates simulation with generative AI tasks. This exploration includes developing unified frameworks for simulations, image rendering, and natural language processing, showcasing the versatile applicability of MPM in tackling contemporary computational challenges.

Political discourse is constantly in flux: the key issues, the actors that define the discourseare changing from moment to moment. Quantitative approaches to discourse are an impor- tant methodological tool that can help researchers measure the structure of and changes in discourse. However, existing approaches are often time-consuming and not scalable to large datasets. In this thesis, I apply a fully automated approach to discourse analysis that combines Structural Topic Modeling and Network Analysis. I apply the model to a novel dataset of the Brexit negotiations in British parliament. I find that the method captures the state of relations between actors and the progress in the negotiation process.

Immigrants have been entering the U.S. since its inception; however, predominant immigration flows have changed over time. Following the 1965 Hart-Celler Act, which repealed national quotas for immigration, two pan-ethnic communities that grew significantly include the Middle Eastern North African (MENA) and South Asian immigrant communities (Bhandari, 2022; Harjanto & Batalova, 2022). MENA and South Asian Americans have established themselves as prominent pan-ethnic communities in the U.S. with a large immigrant network throughout the country (Basu, 2016; Cainkar, 2018; Hashad, 2003; Sekhon, 2003). However, following 9/11 they have also experienced record levels of hate crimes, violence, and discrimination, which have been shown to adversely affected their health and health access (Budiman, 2020; Martin, 2015; Reitmanova & Gustafson, 2008; Samuels et al., 2021; Samari et al., 2020). Other groups of immigrants have also suffered from government policies and practices that were enacted in response to 9/11; debates related to illegal immigration and visa overstays intensified over the following few decades and greatly impacted immigrants from Mexico, Central America, South America, the Caribbean, and all over Asia (Passel & Cohn, 2014). These events have had a “chilling effect” on the psyche of immigrants from MENA and South Asian backgrounds, as well as Latin and Asian immigrants Quesada et al., 2011). In the studies of this dissertation, I examined how factors pertaining to the migration process have shaped health access and sexual and reproductive healthcare (SRH) of immigrant groups in a post-9/11 world—an era in which immigrants of various race/ethnicities have been vilified in a prevailing anti-immigrant sociopolitical climate. In the first two studies of this dissertation, I explored the neighborhood context that MENA and South Asian immigrants resettle into (Aim 1; Chapter 6) and how these environments shape their health access (Aim 2; Chapter 7). In the third study, I focus on the role of citizenship status on contraception use among reproductive-aged (18-44 years) immigrant women (Aim 3; Chapter 8). This dissertation used secondary data from large demographic surveys including the 2020 American Community Survey (ACS) 5-Year Estimates (Aims 1 and 2) and pooled data from 2017 to 2020 waves of the California Health Interview Survey (CHIS). Data from the first study indicated that MENA and South Asian Americans in California are spread across different metropolitan areas of Northern and Southern California and that they have formed four different types of ethnic neighborhoods that follow a few different social and economic pattern, in terms of the density of these specific immigrant groups, their overall foreign-born concentration, and socioeconomic status. The second study indicated that diversity within these groups, including in terms of their health insurance status, can be observed through the distinct type of neighborhoods in which they resettle. For example, among MENA Americans, socioeconomic advantage in a neighborhood was associated with health insurance status. For South Asians, health insurance status was associated with co-ethnic density and foreign-born density. Finally, in the third study of this dissertation I found that nativity and citizenship status were not significantly associated with contraception use, however, there were notable bivariate differences in type of contraception method used by citizenship status. The findings of this dissertation are important for understanding how different aspects of migration shape health of underrepresented immigrant groups, including MENA and South Asian Americans and non-citizen immigrant groups including legal permanent residents (LPRs) and those without a green card. Researchers and policy makers should use the findings of this dissertation to work toward reducing barriers to health access and SRH in immigrant populations.

This thesis presents three significant contributions to the field of machine learning, with a focus on Variational Autoencoders (VAEs), energy-based models, and education simulations. Firstly, we demonstrate the ability to impose substantial structure on the latent space of VAEs, enabling out-of-distribution data generation, structural hypothesis testing, and the production of augmentations in the latent space. These findings give us new ways to structure and interpret the latent space, creating robustness and explainability. Secondly, we identify a state-of-the-art defense technique using the unsupervised learning approach of energy-based models. This technique effectively defends against several poisoning techniques without requiring excessive additional training time or significantly reducing test accuracy. Lastly, we have developed a simulation for educational purposes that aims to model and comprehend the interactions between humans and machines. This simulation, built on causal information, provides insights into the design of practical educational experiments and highlights the challenges associated with implementing a dynamic Intelligent Tutoring System (ITS) in an educational context. Interestingly, our simulation reveals that heuristic methods continue to perform on par with deep learning techniques in the presence of unknown subpopulation distributions and hidden student states. This suggests that despite the rapid advancements in deep learning, heuristic methods retain their effectiveness in certain scenarios.These findings open new avenues for the application of machine learning techniques and provide a solid foundation for future research in these areas.

The recent emergence of the Internet of Things (IoT) has coincided with the popularity of wireless sensor networks (WSNs), which transmit and receive data to and from IoT devices. Wirelessly powered WSNs offer significant advantages over battery-powered WSNs since they do not suffer from issues regarding battery leakage and limited battery life.

This thesis presents two miniaturized wireless and battery-less localization systems for use in WSNs. Both systems comprise a printed circuit board (PCB) having a microchip, on-PCB coils, and resonating capacitors. The microchip, fabricated in the TSMC 180 nm process, is wirelessly powered by an RF signal and transmits back a locked sub-harmonic signal generated from the powering signal, eliminating the need for a power-hungry oscillator. The PCB has a form factor of 17 mm × 12 mm × 0.2 mm. The first system, having a 6 µW power consumption, has been proposed to be used for wireless capsule endoscopy and demonstrates an accuracy of less than 5 mm in ex vivo measurements. Additionally, the system has been verified to detect a motion as small as 50 µm, as well as rates of motion up to 10 bpm. The second system, having a 1.5 µW power consumption, has been proposed to be used for fracture mapping at temperatures up to 250 °C and pressures up to 24 MPa.

Water scarcity is identified as one of the global issues by the United Nations. As per WHO,2.2 billion people lack access to safe drinking water. Over 2 billion people live in countries experiencing high water stress. Almost 80% of wastewater (UNESCO, 2017) flows back into the ecosystem without being treated or reused. Fresh water demand is expected to increase drastically in the upcoming decade. Fresh water production from unconventional water sources such as seawater, brackish water, and ultra-saline water becomes crucial to achieve self-sufficiency. Desalination provides a favorable solution for coastal places like California due to easy access to seawater. Thermal desalination is suitable for treating water with high salinity due to its robustness and can directly utilize renewable sources like solar energy to adhere to sustainability. This study developed and investigated a novel thermal desalination system which encompasses the primary objective of this work. The novel system combines dynamic flash evaporation, a pressure-driven phase change phenomenon to produce vapor from liquid, along with a vapor separation process initiated through tangential injection. The inlet feed water to be treated passes through injection tubes which are connected to injection passages installed tangentially onto a separator tube. Dynamic flashing is initiated by pressure drop due to friction and acceleration in the injection tubes creating a two-phase mixture. Subsequent tangential injection separates the two-phase mixture through centrifugal force. This approach offers a compact system with vapor production and separation processes occurring on the order of several milliseconds. Tap water and seawater were tested with the system. Performance parameters of thermal conversion efficiency to analyze vapor production efficacy and phase separation efficiency to evaluate the purity of the condensate were investigated. Single-stage system was able to achieve up to 98% for thermal and phase separation efficiencies. Further improvement in the purity of the condensate was achieved through a two-stage system, where the entrained droplets along with vapor captured from the first stage undergoes a second round of separation in a stage connected in series. This resulted in condensate with over 99.9% purity. With seawater of 2.5% salt concentration by mass, the condensate obtained achieved salt concentrations lower than 0.02% by mass comparable to that of potable water. With the goal of optimizing the system for varying operating conditions, the dynamic flashing in one of the injection tubes was studied. A visualization study was performed in the injection tubes. Pressure and temperature measurements along the tube were analyzed for different inlet flowrates and liquid temperatures. The pressure and temperature values showed increasing gradients along the tube indicating an increase in vapor production for increasing flowrates and liquid temperatures. The flow regime development due to the vapor production was tracked through high-speed imagery. Visual observations informed complex flow regimes for flashing flow with numerous bubble nucleations and growth throughout the tube. Bulk nucleation showed dominance over wall nucleation. Distinctive flow regimes were observed for tap water and saltwater. Volumetric void fraction measurements were performed using the capacitance impedance technique. Measurements showed an increasing void fraction along the tube supporting visual identification of the flow regimes. Variation of local superheat along the tube showed dependence on the flow regime. The results provide experimental data to aid modeling efforts on flashing flows.

Spintronics harnesses spin degrees of freedom for information storage and processing, offering notable advantages of non-volatility, low power consumption, and fast speed. Topology, as invariant geometric and physical properties under continuous deformation, endows spintronics with higher efficiency and robustness against external perturbations. Protected by real-space topology, magnetic skyrmions are swirling topological spin structures with particle-like properties and potential candidates for high-density, non-volatile storages. As an electrical readout of magnetic skyrmions, the topological Hall effect (THE) is a non-monotonic feature in the Hall signal. However, in the presence of an anomalous Hall effect (AHE), the THE can be easily confused with the non-monotonic co-existence of two AHEs, or artifact of “THE”. Here, we develop systematic methodologies for distinguishing between the two. Genuine THE occurs in the transition region of the AHE, while artifact of “THE” may occur well beyond the saturation of the “AHE component”. Minor loops of genuine THE with AHE are always within the full loop, while minor loops of artifact of “THE” may reveal a single loop that cannot fit into the “AHE component”. The temperature or gate dependence of artifact of “THE” may also be accompanied by a polarity change of the “AHE component”. Our methods may help future researchers ascertain genuine THE for applications of magnetic skyrmions. Protected by k-space topology, magnetic topological insulators (MTI) can apply highly efficient spin-orbit torque (SOT) and manipulate the magnetization with their unique topological surface states. Here, we demonstrate efficient SOT switching of a hard MTI, V-doped (Bi,Sb)2Te3 (VBST) with a large coercive field that can prevent the influence of an external magnetic field. A giant switched anomalous Hall resistance of 9.2 kΩ is realized, among the largest of all SOT systems. The SOT switching current density can be reduced to 2.8×105 A/cm2. Moreover, as the Fermi level is moved away from the Dirac point by both gate and composition tuning, VBST exhibits a transition from edge-state-mediated to surface-state-mediated transport, thus enhancing the SOT effective field to 1.56±0.12 T/(106 A/cm2) and the interfacial charge-to-spin conversion efficiency to 3.9±0.3 nm-1. The findings establish VBST as an extraordinary candidate for energy-efficient magnetic memory devices.