Human facial and bodily expressions play a crucial role in human-human interaction to convey the communicator’s feelings. Being echoed by the influence of human social behavior, recent studies in human-robot interaction (HRI) have investigated how to generate emotional behaviors for social robots. Emotional behaviors can enhance user engagement, allowing the user to interact with robots in a transparent manner. However, they are ambiguous and affected by many factors such as personality traits, cultures, and environments. This paper focuses on developing the robot’s emotional bodily expressions adopting the user’s affective gestures. We propose the behavior selection and transformation model, enabling the robots to incrementally learn from the user’s gestures, to select the user’s habitual behaviors, and to transform the selected behaviors into the robot motions. The experimental results under several scenarios showed that the proposed incremental learning model endows a social robot with the capability of entering into a positive, long-lasting HRI. We have also confirmed that the robot can express emotions through the imitated motions of the user. The robot’s emotional gestures that reflected the interacting partner’s traits were widely accepted within the same cultural group, and perceptible across different cultural groups in different ways.

Cardiac Output (CO) is a key hemodynamic variable that can be estimated in a minimally invasive way via using Arterial Pressure Waveform Analysis (APWA). Many models use circulation mechanics to build the relationship between arterial pressure and CO. In this study, we attempt to apply machine learning and feature engineering to analyze the Arterial Pressure Waveform (APW) and create regression models to predict the CO. We utilize the traditional APWA model knowledge and the time-domain, frequency-domain, and other characteristics of time series data for feature engineering. We present the benchmarking results for several machine learning models using the MIMICII waveform database. We compare the predicted CO values from our proposed models with the “gold standard” TCO (CO measured by intermittent pulmonary artery thermodilution). Our results show that the Random forest model has the most accurate agreement (MSE: 1.421 $\displaystyle \text{L}/\min$, bias: $-0.01\displaystyle \text{L}/\min$, 95% limits of agreement: $-2.35\displaystyle \text{L}/\min$ to $+2.32\displaystyle \text{L}/\min$, percentage error: 39.44%). Notably, the XGBoost model demonstrates good tracking ability with TCO (radius bias: 11.79o, 95% radius limits of agreement: ±28.89°), achieving the clinically acceptable level.

Pushing is one of the fundamental nonprehensile manipulation skills to impart to an object changes in position and orientation. To exploit this skill to manipulate novel objects, explicit knowledge of their physical properties should be given a priori. In this work, we estimate the center of mass (CoM) of an object by narrowing down its probable location with a deep learning model and Mason’s voting theorem. In addition, we propose the Zero Moment Two Edge Pushing (ZMTEP) method to translate a novel object without rotation to a goal pose. The proposed method enables a pusher to select the most suitable two-edge-contact configuration for a given object using the estimated CoM and the geometrical shape of the object. Notably, neither the friction between the object and its support plane nor the friction between the object and the pusher are assumed to be known. We evaluate the proposed CoM estimation and ZMTEP methods through a series of experiments in both simulation and real robotic pusher settings. The result shows that the CoM estimation method has good mean squared error properties and small standard deviation, and the ZMTEP method significantly outperforms competitive baseline methods. Note to Practitioners —This article aims to endow robotic arms with the capability of moving or aligning objects by pushing, which is much more simple and secure than pick-and-place or in-hand manipulations. Most in-demand manipulation skills require sophisticated hand design and control, which might not be affordable for industrial applications staying cost-competitive. In contrast, robot pushing can be implemented with different types of simple pushers and straightforwardly applied to pre-grasp manipulation. This article makes the estimation of an object’s CoM location practical. Building upon the estimation method, a robust and noise-tolerant two-edge-contact pushing configuration selection method is presented to translate an arbitrarily shaped unknown object to its goal pose.