Semantic learning from keyframe demonstration using object attribute constraints.

Learning from demonstration is an approach that allows users to personalize a robot's tasks. While demonstrations often focus on conveying the robot's motion or task plans, they can also communicate user intentions through object attributes in manipulation tasks. For instance, users might want to teach a robot to sort fruits and vegetables into separate boxes or to place cups next to plates of matching colors.

Smartphone-based particle tracking velocimetry for the in vitro assessment of coronary flows.

The present study adopts a smartphone-based approach for the experimental characterization of coronary flows. Technically, Particle Tracking Velocimetry (PTV) measurements were performed using a smartphone camera and a low-power continuous wave laser in realistic healthy and stenosed phantoms of left anterior descending artery with inflow Reynolds numbers approximately ranging from 20 to 200. A Lagrangian-Eulerian mapping was performed to convert Lagrangian PTV velocity data to a Eulerian grid.

Predicting Lipid-Rich Plaque Progression in Coronary Arteries Using Multimodal Imaging and Wall Shear Stress Signatures.

Background: Plaque composition and wall shear stress (WSS) magnitude act as well-established players in coronary plaque progression. However, WSS magnitude per se does not completely capture the mechanical stimulus to which the endothelium is subjected, since endothelial cells experience changes in the WSS spatiotemporal configuration on the luminal surface. This study explores WSS profile and lipid content signatures of plaque progression to identify novel biomarkers of coronary atherosclerosis.

Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers.

Specifying and solving Constraint-based Optimization Problems (COP) has become a mainstream technology for advanced motion control of mobile robots. COP programming still requires expert knowledge to transform specific application context into the right configuration of the COP parameters (i.e.

Smartphone-based particle image velocimetry for cardiovascular flows applications: A focus on coronary arteries.

An experimental set-up is presented for the characterization of the fluid dynamics in personalized phantoms of healthy and stenosed coronary arteries. The proposed set-up was fine-tuned with the aim of obtaining a compact, flexible, low-cost test-bench for biomedical applications. Technically, velocity vector fields were measured adopting a so-called smart-PIV approach, consisting of a smartphone camera and a low-power continuous laser (30 mW).

A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data.

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes.

Particle Filters: A Hands-On Tutorial.

The particle filter was popularized in the early 1990s and has been used for solving estimation problems ever since. The standard algorithm can be understood and implemented with limited effort due to the widespread availability of tutorial material and code examples. Extensive research has advanced the standard particle filter algorithm to improve its performance and applicability in various ways in the years after.