Robotic cloth manipulation for clothing assistance task using Dynamic Movement Primitives

The need of robotic clothing assistance in the field of assistive robotics is growing, as it is one of the most basic and essential assistance activities in daily life of elderly and disabled people. In this study we are investigating the applicability of using Dynamic Movement Primitives (DMP) as a task parameterization model for performing clothing assistance task. Robotic cloth manipulation task deals with putting a clothing article on both the arms. Robot trajectory varies significantly for various postures and also there can be various failure scenarios while doing cooperative manipulation with non-rigid and highly deformable clothing article. We have performed experiments on soft mannequin instead of human. Result shows that DMPs are able to generalize movement trajectory for modified posture.3rd International Conference of Robotics Society of India (AIR \u2717: Advances in Robotics), June 28 - July 2, 2017, New Delhi, Indi

Bayesian Nonparametric Learning of Cloth Models for Real-time State Estimation

Robotic solutions to clothing assistance can significantly improve quality of life for the elderly and disabled. Real-time estimation of the human-cloth relationship is crucial for efficient learning of motor skills for robotic clothing assistance. The major challenge involved is cloth-state estimation due to inherent nonrigidity and occlusion. In this study, we present a novel framework for real-time estimation of the cloth state using a low-cost depth sensor, making it suitable for a feasible social implementation. The framework relies on the hypothesis that clothing articles are constrained to a low-dimensional latent manifold during clothing tasks. We propose the use of manifold relevance determination (MRD) to learn an offline cloth model that can be used to perform informed cloth-state estimation in real time. The cloth model is trained using observations from a motion capture system and depth sensor. MRD provides a principled probabilistic framework for inferring the accurate motion-capture state when only the noisy depth sensor feature state is available in real time. The experimental results demonstrate that our framework is capable of learning consistent task-specific latent features using few data samples and has the ability to generalize to unseen environmental settings. We further present several factors that affect the predictive performance of the learned cloth-state model

Reinforcement Learning of Stable Trajectory for Quasi-Passive Dynamic Walking of an Unstable Biped Robot

Biped walking is one of the major research targets in recent humanoid robotics, and many researchers are now interested in Passive Dynamic Walking (PDW) [McGeer (1990)] rather than that by the conventional Zero Moment Point (ZMP) criterion [Vukobratovic (1972)]. The ZMP criterion is usually used for planning a desired trajectory to be tracked by

1s2s2p23d 6L - 1s2p33d 6D, L=F, D, P Transitions in O IV, F V and Ne VI

We present observations of VUV transitions between doubly excited sextet states in O IV, F V and Ne VI. Spectra were produced by collisions of an O+ beam with a solid carbon target. We also studied spectra obtained previously of F V and Ne VI. Some observed lines were assigned to the 1s2s2p23d 6L - 1s2p33d 6D, L=F, D, P electric-dipole transitions, and compared with results of MCHF (with QED and higher-order corrections) and MCDF calculations. 42 new lines have been identified. Highly excited sextet states in five-electron ions provide a new form of energy storage and are possible candidates for VUV and x-ray lasers.Comment: 11 pages, 12 figure

Numerical Study on Spin Torque Switching in Thermally Activated Region

We studied the spin torque switching of the single free layer in the thermally activated region by numerically solving the Landau-Lifshitz-Gilbert equation. We found that the temperature dependence of the switching time of the in-plane magnetized system is nonlinear, which means $b \neq 1$. Here, $b$ is the exponent of the current term in the switching rate formula and has been widely assumed to be unity. This result enables us to evaluate the thermal stability of spintronics devices.Comment: 3 pages, 4 figure

Predicting Wearing-Off of Parkinson’s Disease Patients Using a Wrist-Worn Fitness Tracker and a Smartphone: A Case Study

Parkinson’s disease (PD) patients experience varying symptoms related to their illness. Therefore, each patient needs a tailored treatment program from their doctors. One approach is the use of anti-PD medicines. However, a “wearing-off” phenomenon occurs when these medicines lose their effect. As a result, patients start to experience the symptoms again until their next medicine intake. In the long term, the duration of “wearing-off” begins to shorten. Thus, patients and doctors have to work together to manage PD symptoms effectively. This study aims to develop a prediction model that can determine the “wearing-off” of anti-PD medicine. We used fitness tracker data and self-reported symptoms from a smartphone application in a real-world environment. Two participants wore the fitness tracker for a month while reporting any symptoms using the Wearing-Off Questionnaire (WoQ-9) on a smartphone application. Then, we processed and combined the datasets for each participant’s models. Our analysis produced prediction models for each participant. The average balanced accuracy with the best hyperparameters was at 70.0–71.7% for participant 1 and 76.1–76.9% for participant 2, suggesting that our approach would be helpful to manage the “wearing-off” of anti-PD medicine, motor fluctuations of PD patients, and customized treatment for PD patients

Predicting sit-to-stand adaptations due to muscle strength deficits and assistance trajectories to complement them

Sit-to-stand (STS) transition is one of the most bio-mechanically challenging task necessary for performing activities of daily life. With muscle strength being the most dominant, many co-occurring factors influence how individuals perform STS. This study investigates the STS changes and STS failure caused by strength deficits using the trajectories generated employing an open-loop single shooting optimization framework and musculoskeletal models. The strength deficits were introduced by simultaneously scaling the maximum isometric strength of muscles in steps of 20%. The optimization framework could generate successful STS transitions for models with up to 60% strength deficits. The joint angle kinematics, muscle activation patterns, and the ground reaction forces from the 0% strength deficit model’s STS transition match those observed experimentally for a healthy adult in literature. Comparison of different strength deficit STS trajectories shows that the vasti muscle saturation leads to reduced activation of the antagonistic hamstring muscle, and consequently, the gluteus maximus muscle saturation. Subsequently, the observation of reduced hamstring activation and the motion tracking results are used to suggest the vasti muscle weakness to be responsible for STS failure. Finally, the successful STS trajectory of the externally assisted 80% strength deficit model is presented to demonstrate the optimization framework’s capability to synthesize assisted STS transition. The trajectory features utilization of external assistance as and when needed to complement strength deficits for successful STS transition. Our results will help plan intervention and design novel STS assistance devices