Explorations in engagement for humans and robots

This paper explores the concept of engagement, the process by which individuals in an interaction start, maintain and end their perceived connection to one another. The paper reports on one aspect of engagement among human interactors--the effect of tracking faces during an interaction. It also describes the architecture of a robot that can participate in conversational, collaborative interactions with engagement gestures. Finally, the paper reports on findings of experiments with human participants who interacted with a robot when it either performed or did not perform engagement gestures. Results of the human-robot studies indicate that people become engaged with robots: they direct their attention to the robot more often in interactions where engagement gestures are present, and they find interactions more appropriate when engagement gestures are present than when they are not.Comment: 31 pages, 5 figures, 3 table

Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance. Source code repository: https://github.com/pschydlo/ActionAnticipationComment: IEEE International Conference on Robotics and Automation (ICRA) 2018, Accepte

Performance of grassed swale as stormwater quantity control in lowland area

Grassed swale is a vegetated open channel designed to attenuate stormwater through infiltration and conveying runoff into nearby water bodies, thus reduces peak flows and minimizes the causes of flood. UTHM is a flood-prone area due to located in lowland area, has high groundwater level and low infiltration rates. The aim of this study is to assess the performance of grassed swale as a stormwater quantity control in UTHM. Flow depths and velocities of swales were measured according to Six-Tenths Depth Method shortly after a rainfall event. Flow discharges of swales (Qswale) were evaluated by Mean- Section Method to determine the variations of Manning’s roughness coefficients (ncalculate) that results between 0.075 – 0.122 due to tall grass and irregularity of channels. Based on the values of Qswale between sections of swales, the percentages of flow attenuation are up to 54%. As for the flow conveyance of swales, Qswale were determined by Manning’s equation that divided into Qcalculate, evaluated using ncalculate, and Qdesign, evaluated using roughness coefficient recommended by MSMA (ndesign), to compare with flow discharges of drainage areas (Qpeak), evaluated by Rational Method with 10-year ARI. Each site of study has shown Qdesign is greater than Qpeak up to 59%. However, Qcalculate is greater than Qpeak only at a certain site of study up to 14%. The values of Qdesign also greater than Qcalculate up to 52% where it shows that the roughness coefficients as considered in MSMA are providing a better performance of swale. This study also found that the characteristics of the studied swales are comparable to the design consideration by MSMA. Based on these findings, grassed swale has the potential in collecting, attenuating, and conveying stormwater, which suitable to be applied as one of the best management practices in preventing flash flood at UTHM campus

Interactive Robot Learning of Gestures, Language and Affordances

A growing field in robotics and Artificial Intelligence (AI) research is human-robot collaboration, whose target is to enable effective teamwork between humans and robots. However, in many situations human teams are still superior to human-robot teams, primarily because human teams can easily agree on a common goal with language, and the individual members observe each other effectively, leveraging their shared motor repertoire and sensorimotor resources. This paper shows that for cognitive robots it is possible, and indeed fruitful, to combine knowledge acquired from interacting with elements of the environment (affordance exploration) with the probabilistic observation of another agent's actions. We propose a model that unites (i) learning robot affordances and word descriptions with (ii) statistical recognition of human gestures with vision sensors. We discuss theoretical motivations, possible implementations, and we show initial results which highlight that, after having acquired knowledge of its surrounding environment, a humanoid robot can generalize this knowledge to the case when it observes another agent (human partner) performing the same motor actions previously executed during training.Comment: code available at https://github.com/gsaponaro/glu-gesture