The Endoscopic Submucosal Dissection (ESD) method is increasingly becoming the method of choice for surgeons attempting to remove precancerous and early-stage cancerous lesions in the lining of the Gastrointestinal (GI) tract. Being an endoscopic procedure, it is less invasive than most other procedures used for tumor removal. However, this procedure has a steep learning curve and a high number of surgical complications. The primary reason for this is the limited ability of the surgeon to retract mucosal (stomach lining) tissue while they dissect under it. Unlike in traditional surgery, the surgeon lacks a second hand to leverage tissue during dissection in endoscopic procedures. This study proposed the deployment of an endoscopic clip to the surface of the lesion. The clip had a permanent magnet connected to it. In addition, a large permanent external magnet mounted to the end-effector of a robotic arm was positioned above the magnetic clip to pull the internal magnet and retract tissue. Magnetic Force simulations were conducted in the design processes for the magnets to determine whether sufficient force for tissue retraction was being achieved. The use of fiber optic shape sensors to track and localize the internal magnet was also explored. Experimental validations of the external and internal magnet designs as well as tracking of the internal magnet were performed in surgical trials on ex-vivo and live porcine models. Compared to traditional ESD, the use of magnetic retraction in ESD significantly improved tissue exposure for dissection, decreased the required time for the dissection stage of the ESD procedure, and reduced the incidence of surgical complications. Therefore, this technology holds substantial potential for enhancing ESD procedures, advancing the non-invasive treatment of colorectal cancer, and potentially improving patient outcomes significantly.

Multiple Sclerosis (MS) is a debilitating neurological disease that affects millions of individuals across the world. There is no current cure for the disease, so much of the patient treatment is focused on management of the disease. One of the potential effects of having MS is having a decrease in balance which leads to a greater risk in sustaining a fall. It has been found in previous studies that MS patients have slower reaction times compared to healthy controls. Furthermore, electromyography (EMG) is an effective way to measure a subject's reaction to a perturbation. This study aims to see if MS subjects can improve their reaction times through a series of perturbation-based training visits. 18 MS patients and 11 healthy controls were recruited for this study. Each subject went through two baseline visits, six training visits, and two post-assessment visits. During each visit, subjects went through a series of forward and backward perturbations from a stand to react position administered by a dual-belt perturbation treadmill. The subjects' reaction times were measured by taking the difference between the onset of the treadmill movement and the onset of the muscle activation. This muscle activation was measured by placing EMG sensors on the tibialis anterior muscle and medial gastrocnemius muscle on each leg. After running a repeated measures ANOVA test, it was found that there were no significant differences in the reaction times between MS participants and healthy controls. However, the overall trend in the data was promising, as MS patients did improve their performance in backward-stepping slightly. Adding more participants to the study could strengthen this trend. It was also found that males across both groups significantly improved their reaction times compared to females. However, it is unknown why this occurred. Future goals would be to add more participants to the study and follow-up with MS patients to see if they have a decrease in falls post-training.

Stroke survivors must overcome motor deficits that greatly impede their balancing ability, thus stunting their independence and overall quality of life. Robot-aided rehabilitation is a new approach to balance therapy presenting notable advantages in efficiency, precision, and consistency. Prior studies have demonstrated the success of visual feedback, force plates, surface perturbations, and compliant surfaces in improving balance control for people with neuromuscular disorders. However, this study is the first to investigate the effect of incorporating each aspect into a stroke balance training program. The side-specific robotic platforms used could generate perturbations while also simulating compliant surfaces. During the 6-week study, 2 subjects each completed 9, 2-hour long training sessions, along with a clinical pre- and post-assessment. Subjects utilized visual feedback of center of pressure and weight distribution to strive for successful balance, and the platforms perturbed if balance was maintained for a sufficient time period. To increase training difficulty, platform stiffness decreased with increased performance. Improvements in functional postural balance for both subjects were demonstrated by the Berg Balance Scale, Mini-BESTest, Timed 10-Meter Walk Test, and 5 Times Sit-to-Stand Test. Decreases in Time to Perturb and Time to Stabilize were suggestive of improved dynamic postural balance. Decreased platform stiffness indicated sustained improvements in increasingly challenging environments, and a 3-month follow up revealed retained functional balance improvements. These results demonstrate the effectiveness of patient-adaptive perturbation-based robotic training on compliant surfaces in improving postural balance for chronic stroke patients.

Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation. Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.

Soft robotics has garnered attention for its substantial prospective in various domains, such as manipulation and interactions with humans, by offering competitive advantages against rigid robotic systems, including inherent compliance and variable stiffness. Despite these benefits, their theoretically infinite degrees of freedom and prominent nonlinearities pose significant challenges in developing dynamic models and guiding the robots along desired paths. Additionally, soft robots may exhibit rigid behaviors and potentially collide with their surroundings during path tracking tasks, particularly when possible contact points are unknown. In this dissertation, reduced-order models are used to describe the behaviors of three different soft robot designs, including both linear parameter varying (LPV) and augmented rigid robot (ARR) models. While the reduced-order model captures the majority of the soft robot's dynamics, modeling uncertainties notably remain. Non-repeated modeling uncertainties are addressed by categorizing them as a lumped disturbance, employing two methodologies, $H_\infty$ method and nonlinear disturbance observer (NDOB) based sliding mode control, for its rejection. For repeated disturbances, an iterative learning control (ILC) with a P-type learning function is implemented to enhance trajectory tracking efficacy. Furthermore,for non-repeated disturbances, the NDOB facilitates the contact estimation, and its results are jointly used with a switching algorithm to modify the robot trajectories. The stability proof of all controllers and corresponding simulation and experimental results are provided. For a path tracking task of a soft robot with multi-segments, a robust control strategy that combines a LPV model with an innovative improved nonlinear disturbance observer-based adaptive sliding mode control (INASMC). The control framework employs a first-order LPV model for dynamic representation, leverages an improved disturbance observer for accurate disturbance forecasting, and utilizes adaptive sliding mode control to effectively counteract uncertainties. The tracking error under the proposed controller is proven to be asymptotically stable, and the controller's effectiveness is is validated with simulation and experimental results. Ultimately, this research mitigates the inherent uncertainty in soft robot modeling, thereby enhancing their functionality in contact-intensive tasks.

As the explorations beyond the Earth's boundaries continue to evolve, researchers and engineers strive to develop versatile technologies capable of adapting to unknown space conditions. For instance, the utilization of Screw-Propelled Vehicles (SPVs) and robotics that utilize helical screws propulsion to transverse planetary bodies is a growing area of interest. An example of such technology is the Extant Exobiology Life Surveyor (EELS), a snake-like robot currently developed by the NASA Jet Propulsion Laboratory (JPL) to explore the surface of Saturn’s moon, Enceladus. However, the utilization of such a mechanism requires a deep and thorough understanding of screw mobility in uncertain conditions. The main approach to exploring screw dynamics and optimal design involves the utilization of Discrete Element Method (DEM) simulations to assess interactions and behavior of screws when interacting with granular terrains. In this investigation, the Simplified Johnson-Kendall-Roberts (SJKR) model is implemented into the utilized simulation environment to account for cohesion effects similar to what is experienced on celestial bodies like Enceladus. The model is verified and validated through experimental and theoretical testing. Subsequently, the performance characteristics of screws are explored under varying parameters, such as thread depth, number of screw starts, and the material’s cohesion level. The study has examined significant relationships between the parameters under investigation and their influence on the screw performance.

Although previous studies have elucidated the role of position feedback in the regulation of movement, the specific contribution of Golgi tendon organs (GTO) in force feedback, especially in stabilizing voluntary limb movements, has remained theoretical due to limitations in experimental techniques. This study aims to establish force feedback regulation mediated by GTO afferent signals in two phases. The first phase of this study consisted of simulations using a neuromusculoskeletal model of the monoarticular elbow flexor (MEF) muscle group, assess the impact of force feedback in maintaining steady state interaction forces against variable environmental stiffness. Three models were trained to accurately reach an interaction force of 40N, 50N and 60N respectively, using a fixed stiffness level. Next, the environment stiffness was switched between untrained levels for open loop (OL) and closed loop (CL) variants of the same model. Results showed that compared to OL, CL showed decreased force deviations by 10.43%, 12.11% and 13.02% for each of the models. Most importantly, it is also observed that in the absence of force feedback, environment stiffness is found to have an effect on the interaction force. In the second phase, human subjects were engaged in experiments utilizing an instrumented elbow exoskeleton that applied loads to the MEF muscle group, closely mimicking the simulation conditions. The experiments consisted of reference, blind and catch trial types, and 3 stiffness levels. Subjects were first trained to reach for a predetermined target force. During catch trials, stiffness levels were randomized between reaches. Responses obtained from these experiments showed that subjects were able to regulate forces with no significant effects of trial type or stiffness level. Since experimental results align closely with that of closed loop model simulations, the presence of force feedback mechanisms mediated by GTO within the human neuromuscular system is established. This study not only unveils the critical involvement of GTO in force feedback but also emphasizes the importance of understanding these mechanisms for developing advanced neuroprosthetics and rehabilitation strategies, shedding light on the intricate interplay between sensory inputs and motor responses in human proprioception.

This thesis presents robust and novel solutions using knowledge distillation with geometric approaches and multimodal data that can address the current challenges in deep learning, providing a comprehensive understanding of the learning process involved in knowledge distillation. Deep learning has attained significant success in various applications, such as health and wellness promotion, smart homes, and intelligent surveillance. In general, stacking more layers or increasing the number of trainable parameters causes deep networks to exhibit improved performance. However, this causes the model to become large, resulting in an additional need for computing and power resources for training, storage, and deployment. These are the core challenges in incorporating such models into small devices with limited power and computational resources. In this thesis, robust solutions aimed at addressing the aforementioned challenges are presented. These proposed methodologies and algorithmic contributions enhance the performance and efficiency of deep learning models. The thesis encompasses a comprehensive exploration of knowledge distillation, an approach that holds promise for creating compact models from high-capacity ones, while preserving their performance. This exploration covers diverse datasets, including both time series and image data, shedding light on the pivotal role of augmentation methods in knowledge distillation. The effects of these methods are rigorously examined through empirical experiments. Furthermore, the study within this thesis delves into the efficient utilization of features derived from two different teacher models, each trained on dissimilar data representations, including time-series and image data. Through these investigations, I present novel approaches to knowledge distillation, leveraging geometric techniques for the analysis of multimodal data. These solutions not only address real-world challenges but also offer valuable insights and recommendations for modeling in new applications.

While wearable soft robots have successfully addressed many inherent design limitations faced by wearable rigid robots, they possess a unique set of challenges due to their soft and compliant nature. Some of these challenges are present in the sensing, modeling, control and evaluation of wearable soft robots. Machine learning algorithms have shown promising results for sensor fusion with wearable robots, however, they require extensive data to train models for different users and experimental conditions. Modeling soft sensors and actuators require characterizing non-linearity and hysteresis, which complicates deriving an analytical model. Experimental characterization can capture the characteristics of non-linearity and hysteresis but requires developing a synthesized model for real-time control. Controllers for wearable soft robots must be robust to compensate for unknown disturbances that arise from the soft robot and its interaction with the user. Since developing dynamic models for soft robots is complex, inaccuracies that arise from the unmodeled dynamics lead to significant disturbances that the controller needs to compensate for. In addition, obtaining a physical model of the human-robot interaction is complex due to unknown human dynamics during walking. Finally, the performance of soft robots for wearable applications requires extensive experimental evaluation to analyze the benefits for the user. To address these challenges, this dissertation focuses on the sensing, modeling, control and evaluation of soft robots for wearable applications. A model-based sensor fusion algorithm is proposed to improve the estimation of human joint kinematics, with a soft flexible robot that requires compact and lightweight sensors. To overcome limitations with rigid sensors, an inflatable soft haptic sensor is developed to enable gait sensing and haptic feedback. Through experimental characterization, a mathematical model is derived to quantify the user's ground reaction forces and the delivered haptic force. Lastly, the performance of a wearable soft exosuit in assisting human users during lifting tasks is evaluated, and the benefits obtained from the soft robot assistance are analyzed.