Learning Lyapunov-Stable Polynomial Dynamical Systems Through Imitation

Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert's behavior. However, relying solely on the expert's data might lead to unsafe actions when the robot deviates from the demonstrated trajectories. Stability guarantees have previously been provided utilizing nonlinear dynamical systems, acting as high-level motion planners, in conjunction with the Lyapunov stability theorem. Yet, these methods are prone to inaccurate policies, high computational cost, sample inefficiency, or quasi stability when replicating complex and highly nonlinear trajectories. To mitigate this problem, we present an approach for learning a globally stable nonlinear dynamical system as a motion planning policy. We model the nonlinear dynamical system as a parametric polynomial and learn the polynomial's coefficients jointly with a Lyapunov candidate. To showcase its success, we compare our method against the state of the art in simulation and conduct real-world experiments with the Kinova Gen3 Lite manipulator arm. Our experiments demonstrate the sample efficiency and reproduction accuracy of our method for various expert trajectories, while remaining stable in the face of perturbations.Comment: In 7th Annual Conference on Robot Learning 2023 Aug 3

Assessment of the Effectiveness of Health Education for Ibs Women by Examining the Frequency and Disturbance of Gi Symptoms, Quality of Life and Days of Drug Use

Background: Irritable bowel syndrome (IBS) is a common and chronic functional disorder, yet few studies have demonstrated the effects of IBS health education. Methods: This study was conducted at the gastrointestinal (GI) clinic of a hospital. A parallel-design control trial for IBS women, in which health education was implemented via individual instruction and the Self-Care Manual for IBS Women, was carried out. The questionnaire utilized in this study covered three areas, namely IBS symptom disturbance and frequency, quality of life, and days of drugs use. The intervention effects were assessed four weeks and eight weeks after the intervention and estimated using the GLMM model (generalized linear mixed model). Results: The experimental and control groups consisted of 31 and 30 participants, respectively. The assessment indicated that health education intervention had significantly reduced symptom frequency in the experimental group after four weeks (β = -2.60, P < 0.01) and after eight weeks (β = -3.30, P < 0.01); significantly reduced symptom disturbances after four weeks (β = -5.01, P < 0.01) and after eight weeks (β = -4.79, P < 0.01). Quality of life for both groups increased after eight weeks, with the experimental group experiencing a greater increase than the control group (β = 15.20, P > 0.05). Drug use decreased by an average of 6.23 days (P < 0.01) and 1.3 days (P > 0.05) in the experimental and control groups, respectively. Conclusions: IBS health education had a positive effect on symptom frequency and disturbance, quality of life and days of drug use

Novel approach for representing, generalising, and quantifying periodic gaits

Our goal is to introduce a novel method for representing, generalising, and comparing gaits; particularly, walking gait. Human walking gaits are a result of complex, interdependent factors that include variations resulting from embodiments, environment and tasks, making techniques that use average template frameworks suboptimal for systematic analysis or corrective interventions. The proposed work aims to devise methodologies for being able to represent gaits and gait transitions such that optimal policies that eliminate the inter-personal variations from tasks and embodiment may be recovered. Our approach is built upon (i) work in the domain of null-space policy recovery and (ii) previous work in generalisation for point-to-point movements. The problem is formalised using a walking phase model, and the null-space learning method is used to generalise a consistent policy from multiple observations with rich variations. Once recovered, the underlying policies (mapped to different gait phases) can serve as reference guideline to quantify and identify pathological gaits while being robust against interpersonal and task variations. To validate our methods, we have demonstrated robustness of our method with simulated sagittal 2-link gait data with multiple ground truth constraints and policies. Pathological gait identification was then tested on real-world human gait data with induced gait abnormality, with the proposed method showing significant robustness to variations in speed and embodiment compared to template based methods. Future work will extend this to kinetic features and higher degree-of-freedom

Improving Reinforcement Learning Training Regimes for Social Robot Navigation

In order for autonomous mobile robots to navigate in human spaces, they must abide by our social norms. Reinforcement learning (RL) has emerged as an effective method to train robot navigation policies that are able to respect these norms. However, a large portion of existing work in the field conducts both RL training and testing in simplistic environments. This limits the generalization potential of these models to unseen environments, and the meaningfulness of their reported results. We propose a method to improve the generalization performance of RL social navigation methods using curriculum learning. By employing multiple environment types and by modeling pedestrians using multiple dynamics models, we are able to progressively diversify and escalate difficulty in training. Our results show that the use of curriculum learning in training can be used to achieve better generalization performance than previous training methods. We also show that results presented in many existing state-of-the art RL social navigation works do not evaluate their methods outside of their training environments, and thus do not reflect their policies' failure to adequately generalize to out-of-distribution scenarios. In response, we validate our training approach on larger and more crowded testing environments than those used in training, allowing for more meaningful measurements of model performance

Exploring the quality of life and its related factors among the elderly

As the rapidly growing population of elderly people in the world that means they would be facing all challenges to their quality of life. As age increases, quality of life is often reported to decline. They are also at risk of mental illness, neurological disorder and more health problems affecting their quality of life. Depression is a common mental disorder among the elderly.  The objective of this study is to understand the relationship between depression and quality of life among the elderly in an Indonesian nursing home. This research use the descriptive correlational and cross-sectional design was applied. There were 114 elderly recruited by convenient sampling. The results of this study showed the mean age of the elderly was 71.2 at the time of data collection ranging from 65 to 76 years old. The variables associated with quality of life were age, gender, education, marital status, ethnicity, chronic disease, and depression status. Moreover, the study found that, as predictors, the variables that influenced the quality of life according to relevance were: age, depression status, and educational level. Age and depression status is recognized as significant predictors of the quality of life among the elderly in an Indonesian nursing home. The result of the study would serve as references to the future and related promotion for the same field of the stud