Augmented and mixed-reality technologies are increasingly recognized as pivotal enablers of human-robot collaboration, offering intuitive visual signals that enhance communication and task execution. Despite their potential, the effective integration and optimization of these visual cues in collaborative environments remain underexplored. This thesis addresses these gaps through comprehensive studies on the design and implementation of innovative communication frameworks for human-robot interaction. Initially, this research identifies and empirically evaluates effective visual signals for human-robot collaboration. Through a comparative analysis of static and dynamic cues within a collaborative object sorting task and using information-theoretic approaches, the influence of these cues on human behavior is quantified. The results demonstrate that a strategic combination of visual signals can significantly enhance task efficiency and reduce cognitive load. Further advancing this field, the thesis introduces SiSCo—a novel framework employing Large Language Models (LLMs) to dynamically generate context-aware visual cues. Experimental validation shows that SiSCo enhances communication within human-robot teams, improving team efficiency by approximately 60\% over conventional natural language signals and significantly reducing cognitive strain, as measured by NASA-TLX metrics. Committed to community development, the implementation and associated resources of SiSCo are made openly accessible, underscoring the approach of blending empirical research, immersive technologies, and computational innovation to advance human-robot collaboration. Building upon this foundational work, the thesis proposes IMMRSY, an immersive mixed-reality system designed to enrich human-robot interactions in the domain of robot learning. This system facilitates intuitive virtual manipulation and promotes efficient, cost-effective data collection methodologies, setting new standards for immersive interaction in robotic systems.

The rapid growth of published research has increased the time and energy researchers invest in literature review to stay updated in their field. While existing research tools assist with organizing papers, providing basic summaries, and improving search, there is a need for an assistant that copilots researchers to drive innovation. In response, we introduce buff, a research assistant framework employing large language models to summarize papers, identify research gaps and trends, and recommend future directions based on semantic analysis of the literature landscape, Wikipedia, and the broader internet. We demo buff through a user-friendly chat interface, powered by a citation network encompassing over 5600 research papers, amounting to over 133 million tokens of textual information. buff utilizes a network structure to fetch and analyze factual scientific information semantically. By streamlining the literature review and scientific knowledge discovery process, buff empowers researchers to concentrate their efforts on pushing the boundaries of their fields, driving innovation, and optimizing the scientific research landscape.

The rapid growth of published research has increased the time and energy researchers invest in literature review to stay updated in their field. While existing research tools assist with organizing papers, providing basic summaries, and improving search, there is a need for an assistant that copilots researchers to drive innovation. In response, we introduce buff, a research assistant framework employing large language models to summarize papers, identify research gaps and trends, and recommend future directions based on semantic analysis of the literature landscape, Wikipedia, and the broader internet. We demo buff through a user-friendly chat interface, powered by a citation network encompassing over 5600 research papers, amounting to over 133 million tokens of textual information. buff utilizes a network structure to fetch and analyze factual scientific information semantically. By streamlining the literature review and scientific knowledge discovery process, buff empowers researchers to concentrate their efforts on pushing the boundaries of their fields, driving innovation, and optimizing the scientific research landscape.

Virtual reality (VR) provides significant opportunities for students to experience immersive education. In VR, students can travel to the international space station, or go through a science experiment at home. However, the current tactile feedback provided by these systems do not feel real. Controllers do not provide the same tactile feedback experienced in the physical world. This dissertation aims to bridge the gap between the virtual and physical learning environments through the development of novel haptic devices capable of emulating tactile sensations found in physical science labs. My research explores haptic devices that can emulate the sensations of fluids in vessels within the virtual environment. Fluid handling is a cornerstone experience of science labs. I also explore how to emulate the handling of other science equipment. I describe and research on four novel devices. These are 1) SWISH: A shifting-weight interface of simulated hydrodynamics for haptic perception of virtual fluid vessels, 2) Geppetteau, 3) Vibr-eau, and 4) Pneutouch. SWISH simulates the sensation of virtual fluids in vessels using a rack and pinion mechanism, while Geppetteau employs a string-driven mechanism to provide haptic feedback for a variety of vessel shapes. Vibr-eau utilizes vibrotactile actuators in the vessel’s interior to emulate the behavior of virtual liquids. Finally, Pneutouch enables users to interact with virtual objects through pneumatic inflatables. Through systematic evaluations and comparisons with baseline comparisons, the usability and effectiveness of these haptic devices in enhancing virtual experiences is demonstrated. The development of these haptic mechanisms and interfaces represents a significant step towards creating transformative educational tools that provide customizable, hands-on learning environments in both Mixed (MR) and Virtual Reality (VR) - now called XR. This dissertation contributes to advancing the field of haptics for virtual education and lays the foundation for future research in immersive learning technologies.

This thesis explores the development and integration of a wrist-worn pneumatic haptic interface, Pneutouch, into multiplayer virtual reality (VR) environments. The study investigates the impact of haptics on multiplayer experiences, with a specific focus on presence, collaboration, and communication. Evaluation and investigation were performed using three mini-games, each targeting specific interactions and investigating presence, collaboration, and communication. It was found that haptics enhanced user presence and object realism, increased user seriousness towards tasks, and shifted the focus of interactions from user-user to user-object. In collaborative tasks, haptics increased realism but did not improve efficiency for simple tasks. In communication tasks, a unique interaction modality, termed "haptic mirroring," was introduced, which explored a new form of communication that could be implemented with haptic devices. It was found that with new communication modalities, users experience an associated learning curve. Together, these findings suggest a new set of multiplayer haptic design considerations, such as how haptics increase seriousness, shift focus from social to physical interactions, generally increase realism but decrease task efficiency, and have associated learning curves. These findings contribute to the growing body of research on haptics in VR, particularly in multiplayer settings, and provide insights that can be further investigated or utilized in the implementation of VR experiences.

Huge advancements have been made over the years in terms of modern image-sensing hardware and visual computing algorithms (e.g. computer vision, image processing, computational photography). However, to this day, there still exists a current gap between the hardware and software design in an imaging system, which silos one research domain from another. Bridging this gap is the key to unlocking new visual computing capabilities for end applications in commercial photography, industrial inspection, and robotics. This thesis explores avenues where hardware-software co-design of image sensors can be leveraged to replace conventional hardware components in an imaging system with software for enhanced reconfigurability. As a result, the user can program the image sensor in a way best suited to the end application. This is referred to as software-defined imaging (SDI), where image sensor behavior can be altered by the system software depending on the user's needs. The scope of this thesis covers the development and deployment of SDI algorithms for low-power computer vision. Strategies for sparse spatial sampling have been developed in this thesis for power optimization of the vision sensor. This dissertation shows how a hardware-compatible state-of-the-art object tracker can be coupled with a Kalman filter for energy gains at the sensor level. Extensive experiments reveal how adaptive spatial sampling of image frames with this hardware-friendly framework offers attractive energy-accuracy tradeoffs. Another thrust of this thesis is to demonstrate the benefits of reinforcement learning in this research avenue. A major finding reported in this dissertation shows how neural-network-based reinforcement learning can be exploited for the adaptive subsampling framework to achieve improved sampling performance, thereby optimizing the energy efficiency of the image sensor. The last thrust of this thesis is to leverage emerging event-based SDI technology for building a low-power navigation system. A homography estimation pipeline has been proposed in this thesis which couples the right data representation with a differential scale-invariant feature transform (SIFT) module to extract rich visual cues from event streams. Positional encoding is leveraged with a multilayer perceptron (MLP) network to get robust homography estimation from event data.

Efficient visual sensing plays a pivotal role in enabling high-precision applications in augmented reality and low-power Internet of Things (IoT) devices. This dissertation addresses the primary challenges that hinder energy efficiency in visual sensing: the bottleneck of pixel traffic across camera and memory interfaces and the energy-intensive analog readout process in image sensors. To overcome the bottleneck of pixel traffic, this dissertation proposes a visual sensing pipeline architecture that enables application developers to dynamically adapt the spatial resolution and update rates for specific regions within the scene. By selectively capturing and processing high-resolution frames only where necessary, the system significantly reduces energy consumption associated with memory traffic. This is achieved by encoding only the relevant pixels from the commercial image sensors with standard raster-scan pixel read-out patterns, thus minimizing the data stored in memory. The stored rhythmic pixel region stream is decoded into traditional frame-based representations, enabling seamless integration into existing video pipelines. Moreover, the system includes runtime support that allows flexible specification of the region labels, giving developers fine-grained control over the resolution adaptation process. Experimental evaluations conducted on a Xilinx Field Programmable Gate Array (FPGA) platform demonstrate substantial reductions of 43-64% in interface traffic, while maintaining controllable task accuracy. In addition to the pixel traffic bottleneck, the dissertation tackles the energy intensive analog readout process in image sensors. To address this, the dissertation proposes aggressive scaling of the analog voltage supplied to the camera. Extensive characterization on off-the-shelf sensors demonstrates that analog voltage scaling can significantly reduce sensor power, albeit at the expense of image quality. To mitigate this trade-off, this research develops a pipeline that allows application developers to adapt the sensor voltage on a frame-by-frame basis. A voltage controller is integrated into the existing Raspberry Pi (RPi) based video streaming pipeline, generating the sensor voltage. On top of that, the system provides a software interface for vision applications to specify the desired voltage levels. Evaluation of the system across a range of voltage scaling policies on popular vision tasks demonstrates that the technique can deliver up to 73% sensor power savings while maintaining reasonable task fidelity.

Traditional sports coaching involves face-to-face instructions with athletes or playingback 2D videos of athletes’ training. However, if the coach is not in the same area as the athlete, then the coach will not be able to see the athlete’s full body and thus cannot give precise guidance to the athlete, limiting the athlete’s improvement. To address these challenges, this paper proposes Augmented Coach, an augmented reality platform where coaches can view, manipulate and comment on athletes’ movement volumetric video data remotely via the network. In particular, this work includes a). Capturing the athlete’s movement video data with Kinects and converting it into point cloud format b). Transmitting the point cloud data to the coach’s Oculus headset via 5G or wireless network c). Coach’s commenting on the athlete’s joints. In addition, the evaluation of Augmented Coach includes an assessment of its performance from five metrics via the wireless network and 5G network environment, but also from the coaches’ and athletes’ experience of using it. The result shows that Augmented Coach enables coaches to instruct athletes from a distance and provide effective feedback for correcting athletes’ motions under the network.

5G Millimeter Wave (mmWave) technology holds great promise for Connected Autonomous Vehicles (CAVs) due to its ability to achieve data rates in the Gbps range. However, mmWave suffers high beamforming overhead and requirement of line of sight (LOS) to maintain a strong connection. For Vehicle-to-Infrastructure (V2I) scenarios, where CAVs connect to roadside units (RSUs), these drawbacks become apparent. Because vehicles are dynamic, there is a large potential for link blockages, which in turn is detrimental to the connected applications running on the vehicle, such as cooperative perception and remote driver takeover. Existing RSU selection schemes base their decisions on signal strength and vehicle trajectory alone, which is not enough to prevent the blockage of links. Most recent CAVs motion planning algorithms routinely use other vehicle's near-future plans, either by explicit communication among vehicles, or by prediction. In this thesis, I make use of this knowledge (of the other vehicle's near future path plans) to further improve the RSU association mechanism for CAVs. I solve the RSU association problem by converting it to a shortest path problem with the objective to maximize the total communication bandwidth. Evaluations of B-AWARE in simulation using Simulated Urban Mobility (SUMO) and Digital twin for self-dRiving Intelligent VEhicles (DRIVE) on 12 highway and city street scenarios with varying traffic density and RSU placements show that B-AWARE results in a 1.05x improvement of the potential datarate in the average case and 1.28x in the best case vs. the state of the art. But more impressively, B-AWARE reduces the time spent with no connection by 48% in the average case and 251% in the best case as compared to the state-of-the-art methods. This is partly a result of B-AWARE reducing almost 100% of blockage occurrences in simulation.