Precise kinematic modeling is critical in calibration and controller design for soft robots, yet remains a challenging issue due to their highly nonlinear and complex behaviors. To tackle the issue, numerous data-driven machine learning approaches have been proposed for modeling nonlinear dynamics. However, these models suffer from prediction uncertainty that can negatively affect modeling accuracy, and uncertainty quantification for kinematic modeling in soft robots is underexplored. In this work, using limited simulation and real-world data, we first investigate multiple linear and nonlinear machine learning models commonly used for kinematic modeling of soft robots. The results reveal that nonlinear ensemble methods exhibit the most robust generalization performance. We then develop a conformal kinematic modeling framework for soft robots by utilizing split conformal prediction to quantify predictive position uncertainty, ensuring distribution-free prediction intervals with a theoretical guarantee.

Most commercially available haptic gloves compromise the accuracy of hand-posture measurements in favor of a simpler design with fewer sensors. While inaccurate posture data is often sufficient for the task at hand in biomedical settings such as VR-therapy-aided rehabilitation, measurements should be as precise as possible to digitally recreate hand postures as accurately as possible. With these applications in mind, we have added extra sensors to the commercially available Dexmo haptic glove by Dexta Robotics and applied kinematic models of the haptic glove and the user's hand to improve the accuracy of hand-posture measurements. In this work, we describe the augmentations and the kinematic modeling approach. Additionally, we present and discuss an evaluation of hand posture measurements as a proof of concept.

Tendon-driven Continuum Robots (TDCRs) are promising candidates for applications in confined spaces due to their unique shape, compliance, and miniaturization capability. Non-parallel tendon routing for TDCRs have shown definite advantages including segments with higher degrees of freedom, larger workspace and higher dexterity. However, most works have focused on parallel tendons to achieve constant-curvature shapes, which yields analytically simple kinematics but overly restricts the design possibilities. We believe this under-utilization of general tendon routing can be attributed to the lack of a general kinematic model that estimates shape from only tendon geometry and displacements. Cosserat rod-based models are capable of modeling general tendon routing, but they require accurate tendon tension measurements and extensive system identification, hindering their usability for design purposes. Recent attempts in developing a kinematic model are limited to simple scenarios like actuation with a single tendon or tendons on perpendicular planes. Moreover, model formulations are often disconnected from hardware, making designs challenging to build under manufacturing constraints. Our first contribution is a novel design for TDCRs based on a synovial universal joint module, which provides a mechanically discretized and feasible design space. Based on the design, our second contribution is the formulation and evaluation of an optimization-based kinematic model, capable of handling actuation of multiple general routed tendons. Lastly, we present an example application of a TDCR designed for gaited locomotion, demonstrating our method's potential for an unified model-based design pipeline.

Tropospheric ozone, known as a concerning air pollutant, has been associated with health issues including asthma, bronchitis, and impaired lung function. The rates at which peroxy radicals react with NO play a critical role in the overall formation and depletion of tropospheric ozone. However, obtaining comprehensive kinetic data for these reactions remains challenging. Traditional approaches to determine rate constants are costly and technically intricate. Fortunately, the emergence of machine learning-based models offers a less resource and time-intensive alternative for acquiring kinetics information. In this study, we leveraged deep reinforcement learning to predict ranges of rate constants (\textit{k}) with exceptional accuracy, achieving a testing set accuracy of 100%. To analyze reactivity trends based on the molecular structure of peroxy radicals, we employed 51 global descriptors as input parameters. These descriptors were derived from optimized minimum energy geometries of peroxy radicals using the quantum composite G3B3 method. Through the application of Integrated Gradients (IGs), we gained valuable insights into the significance of the various descriptors in relation to reaction rates. We successfully validated and contextualized our findings by conducting cross-comparisons with established trends in the existing literature. These results establish a solid foundation for pioneering advancements in chemistry, where computer analysis serves as an inspirational source driving innovation.