This paper presents results of user evaluations with a socially assistive robot companion for older people suffering from mild cognitive impairment (MCI) and living (alone) at home. Within the European FP7 project “CompanionAble” (2008-2012) [1], we developed assistive technologies combining a mobile robot and smart environment with the aim to support these people and assist them living in their familiar home environment. For a final evaluation, user experience studies were conducted with volunteer users who were invited to a test home where they lived and freely used the robot and integrated system over a period of two days. Services provided by the companion robot include reminders of appointments (pre-defined or added by the users themselves or their informal carer) as well as frequent recommendations to specific activities, which were listed e.g. by their family carers. Furthermore, video contact with relatives and friends, a cognitive stimulation game designed especially to counter the progress of cognitive impairments, and the possibility to store personal items with the robot are offered. Recognition of the user entering or leaving the home is triggering situation specific reminders like agenda items due during the (expected) absence, missed calls or items not to be forgotten. Continuing our previous work published in [2], this paper presents detailed description of the implemented assistive functions and results of user studies conducted during April and May 2012 in the smart house of the Dutch project partner Smart Homes in Eindhoven, The Netherlands.

Realization and user evaluation of a companion robot for people with mild cognitive impairments

Recent progress in both AI and robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (a) execute action sequences to complete user requests, (b) efficiently ask questions to resolve user requests, (c) understand human commands given in natural language, and (d) understand hum...

https://journals.sagepub.com/doi/pdf/10.1177/0278364916688949

BWIBots: A platform for bridging the gap between AI and human–robot interaction research:

For human–robot interaction (HRI), perception is one of the most important capabilities. This paper reviews several widely used perception methods of HRI in social robots. Specifically, we investigate general perception tasks crucial for HRI, such as where the objects are located in the rooms, what objects are in the scene, and how they interact with humans. We first enumerate representative social robots and summarize the most three important perception methods from these robots: feature extraction, dimensionality reduction, and semantic understanding. For feature extraction, four widely used signals including visual-based, audio-based, tactile-based and rang sensors-based are reviewed, and they are compared based on their advantages and disadvantages. For dimensionality reduction, representative methods including principle component analysis (PCA), linear discriminant analysis (LDA), and locality preserving projections (LPP) are reviewed. For semantic understanding, conventional techniques for several typical applications such as object recognition, object tracking, object segmentation, and speaker localization are discussed, and their characteristics and limitations are also analyzed. Moreover, several popular data sets used in social robotics and published semantic understanding results are analyzed and compared in light of our analysis of HRI perception methods. Lastly, we suggest important future work to analyze fundamental questions on perception methods in HRI.

A Survey on Perception Methods for Human–Robot Interaction in Social Robots

This paper presents the implementation and evaluation results of the German research project SERROGA (2012 till mid 2015), which aimed at developing a robot companion for domestic health assistance for older people that helps keeping them physically and mentally fit to remain living independently in their own homes for as long as possible. The paper gives an overview of the developed companion robot, its system architecture, and essential skills, behaviors, and services required for a robotic health assistant. Moreover, it presents a new approach allowing a quantitative description and assessment of the navigation complexity of apartments to make them objectively comparable for function tests under real-life conditions. Based on this approach, the results of function tests executed in 12 apartments of project staff and seniors are described. Furthermore, the paper presents findings of a case study conducted with nine seniors (aged 68–92) in their own homes, investigating both instrumental and social-emotional functions of a robotic health assistant. The robot accompanied the seniors in their homes for up to three days assisting with tasks of their daily schedule and health care, without any supervising person being present on-site. Results revealed that the seniors appreciated the robot's health-related instrumental functions and even built emotional bonds with it.

Robot companion for domestic health assistance: Implementation, test and case study under everyday conditions in private apartments

Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed. It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. Extensive simulation and experiment results show that the proposed mobile manipulation system is able to grasp different types of objects autonomously in various simulation and real-world scenarios, verifying the effectiveness of the proposed mobile manipulation system.

/pdf/learning-mobile-manipulation-through-deep-reinforcement-xgqxlqtz3t.pdf

Learning Mobile Manipulation through Deep Reinforcement Learning.

In the last issue of IEEE Robotics & Automation Magazine, we talked about a number of different robots running the robot operating system (ROS). In this issue, we are going to focus on one in particular, the PR2. Willow Garage announced in May the 11 institutions that will get to use a PR2 for free for two years through its PR2 Beta Program (Figure 1). The PR2s all run ROS, and the institutions have all committed to making significant open-source contributions back to the ROS community. ROS has been gaining traction at an astonishing rate since its release in January, but having 11 PR2s at top institutions will accelerate that progress even further. We will give a brief overview of what the PR2 Beta Program recipients are planning to do, and then dive into a concrete example of the use of ROS on the PR2.

S Cousins

Papers

ROS on the PR2 [ROS Topics]