Domestic Robots Research Articles

How artificial intelligence can change the core of marketing theory

Various recently-introduced applications of artificial intelligence (AI) operate at the interface between businesses and consumers. This paper looks at whether these innovations have relevant implications for marketing theory. The latest literature on the connection between AI and marketing has emphasized a great variety of AI applications that qualify this relationship. Based on these studies but focusing only on the applications with a direct impact on the relationship at the very heart of marketing, i.e., the one between firms and consumers, the paper analyzes three categories of AI applications: AI-based shipping-then-shopping, AI-based service robots, and AI-based smart products and domestic robots. The main result of this first analysis is that all three categories have to do, each in their own way, with mass customization. A discussion of this common trait leads us to recognize their ways to mass customization that – unlike the traditional approach developed thanks to flexible automation and product modularity technologies – place the customization process within a broader perspective of consumer needs management. This change in approach means that marketing should focus more on managing consumers’ needs than directly on the satisfaction of those needs. This finding marks a genuine discontinuity that opens up a new space for reflection for scholars and marketing managers alike.

A hardware intelligent processing accelerator for domestic service robots

ABSTRACT We present a method for implementing hardware intelligent processing accelerator on domestic service robots. These domestic service robots support human life; therefore, they are required to recognize environments using intelligent processing. Moreover, the intelligent processing requires large computational resources. Therefore, standard personal computers (PCs) with robot middleware on the robots do not have enough resources for this intelligent processing. We propose a ‘connective object for middleware to an accelerator (COMTA),’ which is a system that integrates hardware intelligent processing accelerators and robot middleware. Herein, by constructing dedicated architecture digital circuits, field-programmable gate arrays (FPGAs) accelerate intelligent processing. In addition, the system can configure and access applications on hardware accelerators via a robot middleware space; consequently, robotic engineers do not require the knowledge of FPGAs. We conducted an experiment on the proposed system by utilizing a human-following application with image processing, which is commonly applied in the robots. Experimental results demonstrated that the proposed system can be automatically constructed from a single-configuration file on the robot middleware and can execute the application 5.2 times more efficiently than an ordinary PC.

Sensor Technologies for Automobiles and Robots

AbstractThis paper reviews various sensor technologies with application in automobiles and robots. Several automotive sensors have been developed using the micro‐electromechanical systems (MEMS) technology. The technologies related to combustion pressure sensors, yaw rate sensors, acceleration sensors, and optical scanners are described here. The combustion pressure sensors are used for engine control, while the yaw rate sensors of quartz and silicon crystals and the acceleration sensors are applied for vehicle motion control. Optical scanners are developed for autonomous driving. Several automotive sensors have been significantly reduced in size and cost, and their performance has been improved dramatically by MEMS technology. The inertial force‐sensing system with yaw rate and acceleration sensors enabled the assistant and partner robots to stand on two legs, walk, or invert and move on two wheels. Because the robots are required to have a variety of sensors, a nerve‐net sensing system was developed. This sensing system has three layers: the host, relay, and sensor nodes. The sensor node is a special large‐scale integration called a ‘nerve‐net chip.’ A triaxial force tactile sensor integrated with the nerve‐net chip was developed as a 2.8‐mm square surface‐mountable chip. A force and thermal tactile sensor was also developed, which detected heat flows and discriminated the materials of contacted objects. The heat flow sensor was thinned with thin‐film technology. The sensor technologies will be used for eco cars and autonomous vehicles. Next‐generation robots are also supported by several automotive sensor technologies. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Shall I Trust You? From Child-Robot Interaction to Trusting Relationships.

Studying trust in the context of human–robot interaction is of great importance given the increasing relevance and presence of robotic agents in the social sphere, including educational and clinical. We investigated the acquisition, loss, and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in vivo. The relationship between trust and the representation of the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. Additionally, to outline children’s beliefs about the mental competencies of the robot, we further evaluated the attribution of mental states to the interactive agent. In general, no substantial differences were found in children’s trust in the play partner as a function of agency (human or robot). Nevertheless, 3-year-olds showed a trend toward trusting the human more than the robot, as opposed to 7-year-olds, who displayed the reverse pattern. These findings align with results showing that, for 3- and 7-year-olds, the cognitive ability to switch was significantly associated with trust restoration in the human and the robot, respectively. Additionally, supporting previous findings, we found a dichotomy between attributions of mental states to the human and robot and children’s behavior: while attributing to the robot significantly lower mental states than the human, in the Trusting Game, children behaved in a similar way when they related to the human and the robot. Altogether, the results of this study highlight that similar psychological mechanisms are at play when children are to establish a novel trustful relationship with a human and robot partner. Furthermore, the findings shed light on the interplay – during development – between children’s quality of attachment relationships and the development of a Theory of Mind, which act differently on trust dynamics as a function of the children’s age as well as the interactive partner’s nature (human vs. robot).

Fast, Light-Responsive, Metal-Like Polymer Actuators Generating High Stresses at Low Strain

Summary Producing lightweight polymeric actuators able to generate high stresses typical of hard metals and/or ceramics remains challenging. The photo-mechanical responses of ultra-drawn ultra-high molecular weight polyethylene (UHMWPE) actuators containing azobenzene photo-switches with symmetrically attached polyethylene (PE) side chains are reported. Long PE side chains promote dispersion within the apolar UHMWPE matrix, and the ultra-drawn films are highly aligned. The ultra-drawn azobenzene-doped UHMWPE films have high Young's moduli (∼100 GPa) and are viscoelastic at room temperature at strains below 1%. The photo-mechanical response of the films is fast ( 6 × 104 Pa (kg m−3)−1) to UV or visible light at a low strain (∼0.06%). The actuator responds to rotating linearly polarized light, causing a photo-induced stress wave response. Such rapid, high-stress, low-strain, photo-mechanical responses are unique in soft polymer systems with physical values approaching hard metals/ceramics.

Derivation and Analysis of Home Robot Care Services

Derivation and Analysis of Home Robot Care Services

A narrative approach to human-robot interaction prototyping for companion robots

AbstractThis paper presents a proof of concept prototype study for domestic home robot companions, using a narrative-based methodology based on the principles of immersive engagement and fictional enquiry, creating scenarios which are inter-connected through a coherent narrative arc, to encourage participant immersion within a realistic setting. The aim was to ground human interactions with this technology in a coherent, meaningful experience. Nine participants interacted with a robotic agent in a smart home environment twice a week over a month, with each interaction framed within a greater narrative arc. Participant responses, both to the scenarios and the robotic agents used within them are discussed, suggesting that the prototyping methodology was successful in conveying a meaningful interaction experience.

5 DOF Home Robot Arm based on Counterbalance Mechanism

5 DOF Home Robot Arm based on Counterbalance Mechanism

Interactive Information Support by Robot Partners Based on Informationally Structured Space

Recently, there has been an increase in the importance of community-centric systems as a new paradigm to enhance the quality of community (QOC). Social media plays an important role in creating, sharing, and exchanging information within a community. However, assistive technologies should be developed from human-centric and community-centric points of view to realize such information support. In this paper, we discuss the use of smart devices interlocked robot partners for interactive information support similar to concierge services in hotels. The interactive information support system is composed of two main subsystems, namely robot partners and informationally structured space servers. A robot partner performs communication and interaction with people through voice recognition and gesture recognition in addition to the use of touch interfaces. The informationally structured space server receives the measurement data of human motions and personal information containing human requests and preferences from the robot partner. Next, the informationally structured space server selects and recommends shops, restaurants, and sightseeing spots to the guests and visitors through utterance and display by the robot partner. First, we explain the concept of the informationally structured space to connect a person with sensory information and propose the overall architecture of the informationally structured space. Next, we explain how to provide information support using smart devices interlocked robot partners based on the informationally structured space. In addition, we describe several social experiments on interactive information support at hotels. Finally, we discuss the effectiveness of the proposed system and the future direction of community-centric systems.

Intrinsic Motivation Based Hierarchical Exploration for Model and Skill Learning

Hierarchical skill learning is an important research direction in human intelligence. However, many real-world problems have sparse rewards and a long time horizon, which typically pose challenges in hierarchical skill learning and lead to the poor performance of naive exploration. In this work, we propose an algorithmic framework called surprise-based hierarchical exploration for model and skill learning (Surprise-HEL). The framework leverages the surprise-based intrinsic motivation for improving the efficiency of sampling and driving exploration. It also combines the surprise-based intrinsic motivation and the hierarchical exploration to speed up the model learning and skill learning. Moreover, the framework incorporates the reward independent incremental learning rules and the technique of alternating model learning and policy update to handle the changing intrinsic rewards and the changing models. These works enable the framework to implement the incremental and developmental learning of models and hierarchical skills. We tested Surprise-HEL on a common benchmark domain: Household Robot Pickup and Place. The evaluation results show that the Surprise-HEL framework can significantly improve the agent’s efficiency in model and skill learning in a typical complex domain.

Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

Nowadays, security guard patrol services are becoming roboticized. However, high construction prices and complex systems make patrol robots difficult to be popularized. In this research, a simplified autonomous patrolling robot is proposed, which is fabricated by upgrading a wheeling household robot with stereo vision system (SVS), radio frequency identification (RFID) module, and laptop. The robot has four functions: independent patrolling without path planning, checking, intruder detection, and wireless backup. At first, depth information of the environment is analyzed through SVS to find a passable path for independent patrolling. Moreover, the checkpoints made with RFID tag and color pattern are placed in appropriate positions within a guard area. While a color pattern is detected by the SVS, the patrolling robot is guided to approach the pattern and check its RFID tag. For more, the human identification function of SVS is used to detect an intruder. While a skeleton information of the human is analyzed by SVS, the intruder detection function is triggered, then the robot follows the intruder and record the images of the intruder. The recorded images are transmitted to a server through Wi-Fi to realize the remote backup, and users can query the recorded images from the network. Finally, an experiment is made to test the functions of the autonomous patrolling robot successfully.

Application of Intelligent Robot Based on Multi-sensor Control in Modern Home Life

In view of the accuracy and rapidity of intelligent robot information processing, this paper designs a multi-sensor hardware system of robots by using ultrasonic sensors, infrared sensors, Hall sensors, contact sensors and other sensors, and focuses on multi-sensor data. A hybrid data fusion structure and a data fusion algorithm using Bayesian inference algorithm are constructed. The fuzzy obstacle avoidance algorithm is proposed and designed to solve the problem of robot obstacle avoidance. The hardware experimental environment of the system, the experimental results verify the effectiveness of the above-mentioned robot control function implementation. After experimental monitoring, the smart home robot has the advantages of low cost, multi-function, easy maintenance and convenient upgrade, and realizes functions such as voice recognition, home entertainment, indoor cleaning, security detection and alarm.

A Systematic Review of Robotics Research in Support of In-Home Care for Older Adults

The aging population is growing at an unprecedented rate globally and robotics-enabled solutions are being developed to provide better independent living for older adults. In this study, we report the results from a systematic review of the state-of-the-art in home robotics research for caring for older adults. This review aims to address two questions: (1) What research is being done towards integrating robotics for caring for older adults? (2) What are the research and technology challenges that robots are facing in the home? Sixty-three papers have been identified and studied in this review by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Common themes that are consistent across the reviewed papers are distinguished and consolidated as follows: (1) Ambient assisted living, where smart home environments and physical support tools are studied; (2) Robot ecosystem, where robotic devices are used to provide various services; (3) Social interaction, where the social isolation problem has been targeted. We also summarize the results of similar literature reviews we came across during our search. The results of this study present the current research trends and technologies used in each category. The challenges and limitations of robotics applications are also identified. Suggestions for accelerating the deployment of robots at home for providing older adults with independent care in the home are presented based on the results and insights from this study.

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface.

The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension as well as scope. Under the background of artificial intelligence, medical and nonmedical robots have rapidly developed and have gradually been applied to enhance the quality of people's lives. We focus on connecting the brain with a mobile home robot by translating brain signals to computer commands to build a brain-computer interface that may offer the promise of greatly enhancing the quality of life of disabled and able-bodied people by considerably improving their autonomy, mobility, and abilities. Several types of robots have been controlled using BCI systems to complete real-time simple and/or complicated tasks with high performances. In this paper, a new EEG-based intelligent teleoperation system was designed for a mobile wall-crawling cleaning robot. This robot uses crawler type instead of the traditional wheel type to be used for window or floor cleaning. For EEG-based system controlling the robot position to climb the wall and complete the tasks of cleaning, we extracted steady state visually evoked potential (SSVEP) from the collected electroencephalography (EEG) signal. The visual stimulation interface in the proposed SSVEP-based BCI was composed of four flicker pieces with different frequencies (e.g., 6 Hz, 7.5 Hz, 8.57 Hz, and 10 Hz). Seven subjects were able to smoothly control the movement directions of the cleaning robot by looking at the corresponding flicker using their brain activity. To solve the multiclass problem, thereby achieving the purpose of cleaning the wall within a short period, the canonical correlation analysis (CCA) classification algorithm had been used. Offline and online experiments were held to analyze/classify EEG signals and use them as real-time commands. The proposed system was efficient in the classification and control phases with an obtained accuracy of 89.92% and had an efficient response speed and timing with a bit rate of 22.23 bits/min. These results suggested that the proposed EEG-based clean robot system is promising for smart home control in terms of completing the tasks of cleaning the walls with efficiency, safety, and robustness.

For Motion Assistance Humans Prefer to Rely on a Robot Rather Than on an Unpredictable Human.

Objective: The last decades have seen a surge of robots for physical training and work assistance. How to best control these interfaces is unknown, although arguably the interaction should be similar to human movement assistance. Methods: We compare the behaviour and assessment of subjects tracking a moving target with assistance from (i) trajectory guidance (as typically used in robots for physical training), (ii) a human partner, and (iii) the reactive robot partner of Takagi et al. Results: Trajectory guidance was recognised as robotic, while the robot partner was felt as human-like. However, trajectory guidance was preferred to assistance from a human partner, which was recognised as less predictable. The robot partner also was felt to be more predictable and helpful than a human partner, and was preferred. Conclusions: While subjects like to rely on predictable interaction, such as in trajectory guidance, the control reactivity of the robot partner is essential for perceiving an interaction as human-like.

Motion Planning of Tidying up a Chair Based on the Interactive Relationship of Household Objects by a Home Robot

Motion Planning of Tidying up a Chair Based on the Interactive Relationship of Household Objects by a Home Robot

Building semantic grid maps for domestic robot navigation

This article proposes a semantic grid mapping method for domestic robot navigation. Occupancy grid maps are sufficient for mobile robots to complete point-to-point navigation tasks in 2-D small-scale environments. However, when used in the real domestic scene, grid maps are lack of semantic information for end users to specify navigation tasks conveniently. Semantic grid maps, enhancing the occupancy grid map with the semantics of objects and rooms, endowing the robots with the capacity of robust navigation skills and human-friendly operation modes, are thus proposed to overcome this limitation. In our method, an object semantic grid map is built with low-cost sonar and binocular stereovision sensors by correctly fusing the occupancy grid map and object point clouds. Topological spaces of each object are defined to make robots autonomously select navigation destinations. Based on the domestic common sense of the relationship between rooms and objects, topological segmentation is used to get room semantics. Our method is evaluated in a real homelike environment, and the results show that the generated map is at a satisfactory precision and feasible for a domestic mobile robot to complete navigation tasks commanded in natural language with a high success rate.

Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks

As vision and language processing techniques have made great progress, mapless-visual navigation is occupying uppermost position in domestic robot field. However, most current end-to-end navigation models tend to be strictly trained and tested on identical datasets with stationary structure, which leads to great performance degradation when dealing with unseen targets and environments. Since the targets of same category could possess quite diverse features, generalization ability of these models is also limited by their visualized task description. In this article we propose a model-agnostic metalearning based text-driven visual navigation model to achieve generalization to untrained tasks. Based on meta-reinforcement learning approach, the agent is capable of accumulating navigation experience from existing targets and environments. When applied to finding a new object or exploring in a new scene, the agent will quickly learn how to fulfill this unfamiliar task through relatively few recursive trials. To improve learning efficiency and accuracy, we introduce fully convolutional instance-aware semantic segmentation and Word2vec into our DRL network to respectively extract visual and semantic features according to object class, creating more direct and concise linkage between targets and their surroundings. Several experiments have been conducted on realistic dataset Matterport3D to evaluate its target-driven navigation performance and generalization ability. The results demonstrate that our adaptive navigation model could navigate to text-defined targets and achieve fast adaption to untrained tasks, outperforming other state-of-the-art navigation approaches.

Apply Importance Performance Analysis to Explore Innovation Resistance of Home Robot

Apply Importance Performance Analysis to Explore Innovation Resistance of Home Robot

Technology trends and technology predictions in latest technical topics - Selecting technologies to invest in, overviewing the future of home robots -

Technology trends and technology predictions in latest technical topics - Selecting technologies to invest in, overviewing the future of home robots -