There's been a lot of research on how humans interact with robots. In fact, there's a whole field for it called HRI (Human-Robot Interaction), with its own flagship conference (that IEEE co-sponsors) going on right now in New Zealand. The majority of the research in this field focuses on how humans interact with social robots, including home robots, commercial robots, and educational robots and toys, but odds are, if you personally own a robot, it's going to be either a vacuum or a drone. As drones have become more and more pervasive over the last few years, HRI research on them has been expanding. The latest contribution to this area is a fascinating paper being presented at the HRI conference on "Emotion Encoding in Human-Drone Interaction."

Video Friday is your weekly selection of awesome robotics videos, collected by your mysophobic Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. XDog is a small electric quadruped designed and built by Xing Wang, a graduate student at Shanghai University, with support from his adviser Jia Wenchuan. The robot has 12 motors (each leg has 3 DoF), and uses force sensors on each foot, IMU, and joint-angle sensors for control.

Video Friday is your weekly selection of awesome robotics videos, collected by your starving Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Domino's in New Zealand (or Australia, we're not sure) has developed this pizza-delivery robot and I can't tell if they're serious or not: The New Zealand government at least, is taking them seriously, according to Stuff.co.nz: Transport Minister Simon Bridges said Domino's had made contact "a few weeks ago" to inform the Government about DRU and see if New Zealand was interested in hosting trials.

For many people with dementia, moving out of their house and into a care home can be an inevitable and devastating side effect. Spatial and visual problems can accompany the more well-known memory loss, making it difficult for people to get around their once familiar homes. But a new app has been designed to help carers for people with dementia work out how to arrange furniture in their houses, which could allow their loved ones to stay at home for longer. The app will suggest improvements to make carers' homes more accessible to those with dementia. The'Dementia-Friendly Home' app, launched today, uses interactive 3D game technology to come up with ideas for carers to make their homes more accessible for those with dementia.

Suresh Venkatasubramanian of the University of Utah presented a method for finding disparate impact in algorithms last year at the ACM Conference on Knowledge Discovery and Data Mining. If you have ever searched for hotel rooms online, you have probably had this experience: surf over to another website to read a news story and the page fills up with ads for travel sites, offering deals on hotel rooms in the city you plan to visit. Buy something on Amazon, and ads for similar products will follow you around the Web. The practice of profiling people online means companies get more value from their advertising dollars and users are more likely to see ads that interest them. The practice has a downside, though, when the profiling is based on sensitive attributes, such as race, sex, or sexual orientation.

We have developed a visualization system of dementia care skills based on multimodal communication features. The purpose of our system is to provide effective learning of dementia care to trainees. As dementia care skills are difficult to visualize and describe, they are hard to acquire for trainees. We focus on HumanitudeR; a non-pharmacological comprehensive intervention with verbal and non-verbal communication, which is a care methodology of French-origin for the vulnerable elderlies. The multimodal methodology utilizes four techniques to relate to elderly with dementia (i.e., gaze, speak, touch, opportunities to stand on their feet). We analyzed the care videos of Humanitude instructors to extract multimodal communication features. We designed and filmed video contents demonstrating the extracted features. These have shown to be effective, in combination with practice and reflection, to acquire dementia care skills. The trainees could use the system for self-reflection and teaching.

Robots collaborating with humans need to represent knowledge, reason, and learn, at the sensorimotor level and the cognitive level. This paper summarizes the capabilities of an architecture that combines the comple- mentary strengths of declarative programming, proba- bilistic graphical models, and reinforcement learning, to represent, reason with, and learn from, qualitative and quantitative descriptions of incomplete domain knowledge and uncertainty. Representation and reasoning is based on two tightly-coupled domain representations at different resolutions. For any given task, the coarse- resolution symbolic domain representation is translated to an Answer Set Prolog program, which is solved to provide a tentative plan of abstract actions, and to explain unexpected outcomes. Each abstract action is implemented by translating the relevant subset of the corresponding fine-resolution probabilistic representation to a partially observable Markov decision process (POMDP). Any high probability beliefs, obtained by the execution of actions based on the POMDP policy, update the coarse-resolution representation. When incomplete knowledge of the rules governing the domain dynamics results in plan execution not achieving the desired goal, the coarse-resolution and fine-resolution representations are used to formulate the task of incrementally and interactively discovering these rules as a reinforcement learning problem. These capabilities are illustrated in the context of a mobile robot deployed in an indoor office domain.

Certain stimuli in the environment reliably, and perhaps inevitably, trigger human cognitive and behavioral responses. We suggest that the presence of such “trigger stimuli” in modern robots can have disconcerting consequences. We provide one new example of such consequences: a reversal of a pattern of moral judgments people make about robots, depending on whether they view a “mechanical” or a “humanoid” robot.

The increasing mere presence of robots in everyday life does not automatically result in gradual acceptance of these systems by human users. Over the past years, we have conducted several studies with the goal to provide insight into the long-term process of social robots in domestic environments. This paper presents our overall conclusions from the combined findings of our multiple studies on social robot acceptance. We will provide insights from a user’s perspective of what makes robots social, describe a phased framework of the long-term process of robot acceptance, present some key factors for social robot acceptance, offer guidelines to build better sociable robots, and provide some recommendations for conducting research in domestic environments. With sharing our experiences with conducting (long-term) user studies in domestic environments, we aim to serve to push this sub-field of HRI in real-world contexts forward and thereby the community at large.

Sparse coding (Sc) has been studied very well as a powerful data representation method. It attempts to represent the feature vector of a data sample by reconstructing it as the sparse linear combination of some basic elements, and a $L_2$ norm distance function is usually used as the loss function for the reconstruction error. In this paper, we investigate using Sc as the representation method within multi-instance learning framework, where a sample is given as a bag of instances, and further represented as a histogram of the quantized instances. We argue that for the data type of histogram, using $L_2$ norm distance is not suitable, and propose to use the earth mover's distance (EMD) instead of $L_2$ norm distance as a measure of the reconstruction error. By minimizing the EMD between the histogram of a sample and the its reconstruction from some basic histograms, a novel sparse coding method is developed, which is refereed as SC-EMD. We evaluate its performances as a histogram representation method in tow multi-instance learning problems --- abnormal image detection in wireless capsule endoscopy videos, and protein binding site retrieval. The encouraging results demonstrate the advantages of the new method over the traditional method using $L_2$ norm distance.