The effects of physical embodiment and physical presence were explored through a survey of 33 experimental works comparing how people interacted with physical robots and virtual agents. A qualitative assessment of the direction of quantitative effects demonstrated that robots were more persuasive and perceived more positively when physically present in a user?s environment than when digitally-displayed on a screen either as a video feed of the same robot or as a virtual character analog; robots also led to better user performance when they were collocated as opposed to shown via video on a screen. However, participants did not respond differently to physical robots and virtual agents when both were displayed digitally on a screen - suggesting that physical presence, rather than physical embodiment, characterizes people?s responses to social robots. Implications for understanding psychological response to physical and virtual agents and for methodological design are discussed. Survey identified 33 works exploring user responses to physical robots and virtual agents.Robot agents had greater influence when physically present than telepresent.No differences were found between physical robots displayed on a screen and virtual agents that looked similar.Physical presence, but not physical embodiment alone, resulted in more favorable responses from participants.

The benefit of being physically present

The screen shows a bag of popcorn on a table. Several kernels have fallen from the bag. An adult, watching with preschool children, asks a question about the picture: "What will happen to the popcorn if I pick up the television set and turn it upside down?" A lot of kids say that the rest of the popcorn will spill out of the bag. While interesting, this mistake is not really surprising. Children often take television literally. They'll soon be older, and will be wise enough to know better.

Media Equation 研究の背景と動向

Technological innovations, from ubiquitous computing, augmented reality, telecommunication to intelligent appliances and robotics, bring new possibilities to the Smart Home domain, which has led to an increase in the number of academic publications in this domain. To date, no comprehensive overview and clustering of the core concepts used in these publications have been produced. Based on an extensive review of existing literature on the Smart Home, this paper visualizes the state of the art in the Smart Home research in a systematic way and outlines future research challenges. To do so, a business model framework is applied that helps researchers place their work within a broader context and identify gaps in the existing body of knowledge in this area. In order to move from the exploration towards the exploitation of Smart Home concepts, it is essential to contribute to a coherent body of knowledge that not only is technology driven, as it is the case now, but also pay attention to the non-technological ...

https://journals.sagepub.com/doi/pdf/10.1177/1420326X13516350

What we do – and don’t – know about the Smart Home: An analysis of the Smart Home literature:

Pervasive gaming: Status, trends and design principles

Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a deep neural network to predict a measure that candidate motion data for an end effector of a robot will result in a successful grasp of one or more objects by the end effector. Some implementations are directed to utilization of the trained deep neural network to servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector. For example, the trained deep neural network may be utilized in the iterative updating of motion control commands for one or more actuators of a robot that control the pose of a grasping end effector of the robot, and to determine when to generate grasping control commands to effectuate an attempted grasp by the grasping end effector.

Deep machine learning methods and apparatus for robotic grasping

In an image recognition method executed by a computer of an image recognizer using a convolutional neural network, the convolutional neural network is a first convolutional neural network in which a fully connected layer is changed to a convolutional layer, and the method includes controlling a first convolutional neural network to acquire an input image, controlling the first convolutional neural network to estimate a center area of a recognition target in the acquired input image and to output a value indicating the estimated center area as a location of the recognition target in the input image.

Image recognition method

Treasure is a pervasive game playing in the context of people's daily living environments. Unlike previous pervasive games that are based on the predefined contents and proprietary devices, Treasure exploits the "design-in-play" concept to enhance the variability of a game in mixed-reality environments. Dynamic and personalized role design and allocation by players is enabled by exploring local smart objects as game props. The variability of the game is also enhanced by several other aspects, such as user-oriented context-aware action setting and playing environment redeployment. The effectiveness of the "design-in-play" concept is validated through a user study, where 15 subjects were recruited to play and author the trial game.

/pdf/design-in-play-improving-the-variability-of-indoor-pervasive-3z4khnlygu.pdf

Design-in-play: improving the variability of indoor pervasive games

There are provided an apparatus, a method, and an autonomous moving body which allow a recognition target to be notified with certainty that the recognition target is recognized by an autonomous moving body. A recognition result presenting apparatus according to an embodiment of the present disclosure detects a recognition target (person) present within a predetermined range from an automatic driving vehicle and presents, to the detected recognition target (person), the result of recognition indicating that the automatic driving vehicle recognizes the recognition target (person).

Apparatus for presenting result of recognizing recognition target

A system has been developed that can project an embodied agent's image and sound anywhere in a room. It can thus overcome the problems inherent to other embodied agents in dealing with 2D on-screen information and 3D physical information simultaneously. An experiment demonstrated that this ‘PROT’ agent can effectively present both on-screen information and real-world physical information. Because the PROT agent combines the advantages of a robot agent with those of an on-screen agent, it should improve human-robot interaction.

PROT — An embodied agent for intelligible and user-friendly human-robot interaction

An information presentation control apparatus includes a selection information obtainer and a presentation controller. The selection information obtainer obtains selection information representing a selection status of a plurality of recognizers that recognize different targets in surroundings of an autonomous vehicle. The presentation controller causes a presentation device mounted in the autonomous vehicle to present driving information in accordance with the selection information, the driving information being based on at least one of control that is executable by the autonomous vehicle and control that is not executable by the autonomous vehicle and being information about at least one of driving by an automated driving system of the autonomous vehicle and driving by a driver.

Ryota Fujimura

Papers

Image recognition method

Design-in-play: improving the variability of indoor pervasive games

Apparatus for presenting result of recognizing recognition target

PROT — An embodied agent for intelligible and user-friendly human-robot interaction

Information presentation control apparatus, autonomous vehicle, and autonomous-vehicle driving support system