An Automated Planning Model for HRI: Use Cases on Social Assistive Robotics

Using Automated Planning for the high level control of robotic architectures is becoming very popular thanks mainly to its capability to define the tasks to perform in a declarative way. However, classical planning tasks, even in its basic standard Planning Domain Definition Language (PDDL) format, are still very hard to formalize for non expert engineers when the use case to model is complex. Human Robot Interaction (HRI) is one of those complex environments. This manuscript describes the rationale followed to design a planning model able to control social autonomous robots interacting with humans. It is the result of the authors’ experience in modeling use cases for Social Assistive Robotics (SAR) in two areas related to healthcare: Comprehensive Geriatric Assessment (CGA) and non-contact rehabilitation therapies for patients with physical impairments. In this work a general definition of these two use cases in a unique planning domain is proposed, which favors the management and integration with the software robotic architecture, as well as the addition of new use cases. Results show that the model is able to capture all the relevant aspects of the Human-Robot interaction in those scenarios, allowing the robot to autonomously perform the tasks by using a standard planning-execution architecture.

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Reports on the 2014 AAAI Fall Symposium Series

AI Magazine ◽

10.1609/aimag.v36i3.2607 ◽

2015 ◽

Vol 36 (3) ◽

pp. 107-112

Author(s):

Adam B. Cohen ◽

Sonia Chernova ◽

James Giordano ◽

Frank Guerin ◽

Kris Hauser ◽

...

Keyword(s):

Artificial Intelligence ◽

Health Informatics ◽

Autonomous Robots ◽

Human Robot Interaction ◽

Energy Market ◽

Washington Dc ◽

Robot Interaction ◽

Language Access ◽

Symposium Series ◽

And Behavior

The AAAI 2014 Fall Symposium Series was held Thursday through Saturday, November 13–15, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction, Energy Market Prediction, Expanding the Boundaries of Health Informatics Using AI, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences, Natural Language Access to Big Data, and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report.

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Robotics for Assisting Children with Physical and Cognitive Disabilities

Assistive Technologies for Physical and Cognitive Disabilities - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-4666-7373-1.ch005 ◽

2015 ◽

pp. 78-120 ◽

Cited By ~ 4

Author(s):

Mark Tee Kit Tsun ◽

Lau Bee Theng ◽

Hudyjaya Siswoyo Jo ◽

Patrick Then Hang Hui

Keyword(s):

Mental Health ◽

Cerebral Palsy ◽

Social Interaction ◽

Assistive Technologies ◽

Human Robot Interaction ◽

Assistive Robotics ◽

Active Movement ◽

Robot Interaction ◽

Passive Stretching ◽

Locomotion Training

This chapter summarizes the findings of a study on robotics research and application for assisting children with disabilities between the years 2009 and 2013. The said disabilities include impairment of motor skills, locomotion, and social interaction that is commonly attributed to children suffering from Autistic Spectrum Disorders (ASD) and Cerebral Palsy (CP). As opposed to assistive technologies for disabilities that largely account for restoration of physical capabilities, disabled children also require dedicated rehabilitation for social interaction and mental health. As such, the breadth of this study covers existing efforts in rehabilitation of both physical and socio-psychological domains, which involve Human-Robot Interaction. Overviewed topics include assisted locomotion training, passive stretching and active movement rehabilitation, upper-extremity motor function, social interactivity, therapist-mediators, active play encouragement, as well as several life-long assistive robotics in current use. This chapter concludes by drawing attention to ethical and adoption issues that may obstruct the field's effectiveness.

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Multiscale Entropy Quantifies the Differential Effect of the Medium Embodiment on Older Adults Prefrontal Cortex during the Story Comprehension: A Comparative Analysis

Entropy ◽

10.3390/e21020199 ◽

2019 ◽

Vol 21 (2) ◽

pp. 199 ◽

Cited By ~ 4

Author(s):

Soheil Keshmiri ◽

Hidenobu Sumioka ◽

Ryuji Yamazaki ◽

Hiroshi Ishiguro

Keyword(s):

Prefrontal Cortex ◽

Interactive Media ◽

Human Robot Interaction ◽

Assistive Robotics ◽

Multiscale Entropy ◽

Story Comprehension ◽

Complementary Information ◽

Robot Interaction ◽

Activation Patterns ◽

Age Related

Todays’ communication media virtually impact and transform every aspect of our daily communication and yet the extent of their embodiment on our brain is unexplored. The study of this topic becomes more crucial, considering the rapid advances in such fields as socially assistive robotics that envision the use of intelligent and interactive media for providing assistance through social means. In this article, we utilize the multiscale entropy (MSE) to investigate the effect of the physical embodiment on the older people’s prefrontal cortex (PFC) activity while listening to stories. We provide evidence that physical embodiment induces a significant increase in MSE of the older people’s PFC activity and that such a shift in the dynamics of their PFC activation significantly reflects their perceived feeling of fatigue. Our results benefit researchers in age-related cognitive function and rehabilitation who seek for the adaptation of these media in robot-assistive cognitive training of the older people. In addition, they offer a complementary information to the field of human-robot interaction via providing evidence that the use of MSE can enable the interactive learning algorithms to utilize the brain’s activation patterns as feedbacks for improving their level of interactivity, thereby forming a stepping stone for rich and usable human mental model.

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Automated Planning for Robotics

Annual Review of Control Robotics and Autonomous Systems ◽

10.1146/annurev-control-082619-100135 ◽

2020 ◽

Vol 3 (1) ◽

pp. 417-439 ◽

Cited By ~ 1

Author(s):

Erez Karpas ◽

Daniele Magazzeni

Keyword(s):

Motion Planning ◽

Automated Planning ◽

Planning Model ◽

Ai Planning ◽

Different Types ◽

The Right ◽

High Level ◽

And Robotics

Modern robots are increasingly capable of performing “basic” activities such as localization, navigation, and motion planning. However, for a robot to be considered intelligent, we would like it to be able to automatically combine these capabilities in order to achieve a high-level goal. The field of automated planning (sometimes called AI planning) deals with automatically synthesizing plans that combine basic actions to achieve a high-level goal. In this article, we focus on the intersection of automated planning and robotics and discuss some of the challenges and tools available to employ automated planning in controlling robots. We review different types of planning formalisms and discuss their advantages and limitations, especially in the context of planning robot actions. We conclude with a brief guide aimed at helping roboticists choose the right planning model to endow a robot with planning capabilities.

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An innovative high-level human-robot interaction for disabled persons

IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 ◽

10.1109/robot.2004.1307406 ◽

2004 ◽

Cited By ~ 8

Author(s):

P. Nilas ◽

P. Rani ◽

N. Sarkar

Keyword(s):

Disabled Persons ◽

Human Robot Interaction ◽

Robot Interaction ◽

High Level

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Control of Autonomous Robots Using the Principles of Neuromodulation

Volume 2: Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems; Estimation and Id of Energy Systems; Fault Detection; Flow and Thermal Systems; Haptics and Hand Motion; Human Assistive Systems and Wearable Robots; Instrumentation and Characterization in Bio-Systems; Intelligent Transportation Systems; Linear Systems and Robust Control; Marine Vehicles; Nonholonomic Systems ◽

10.1115/dscc2013-4107 ◽

2013 ◽

Cited By ~ 1

Author(s):

Akimul Prince ◽

Biswanath Samanta

Keyword(s):

Neural Network ◽

Autonomous Robots ◽

Neuronal Model ◽

Autonomous Robot ◽

Human Robot Interaction ◽

Robot Interaction ◽

Control Approach ◽

The Neural Network ◽

Simple Neural Network ◽

Interesting Behavior

The paper presents a control approach based on vertebrate neuromodulation and its implementation on an autonomous robot platform. A simple neural network is used to model the neuromodulatory function for generating context based behavioral responses to sensory signals. The neural network incorporates three types of neurons — cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behavior. The implementation of the neuronal model on a relatively simple autonomous robot illustrates its interesting behavior adapting to changes in the environment. The integration of neuromodulation based robots in the study of human-robot interaction would be worth considering in future.

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Speed Adaptation in Learning from Demonstration through Latent Space Formulation

Robotica ◽

10.1017/s0263574719001449 ◽

2019 ◽

Vol 38 (10) ◽

pp. 1867-1879 ◽

Cited By ~ 1

Author(s):

Maria Koskinopoulou ◽

Michail Maniadakis ◽

Panos Trahanias

Keyword(s):

Human Robot Interaction ◽

Space Representation ◽

Learning From Demonstration ◽

Level Control ◽

Physical Constraints ◽

Spatial Features ◽

Spatial Consistency ◽

Latent Space ◽

Space Formulation ◽

High Level

SUMMARYPerforming actions in a timely manner is an indispensable aspect in everyday human activities. Accordingly, it has to be present in robotic systems if they are going to seamlessly interact with humans. The current work addresses the problem of learning both the spatial and temporal characteristics of human motions from observation. We formulate learning as a mapping between two worlds (the observed and the action ones). This mapping is realized via an abstract intermediate representation termed “Latent Space.” Learned actions can be subsequently invoked in the context of more complex human–robot interaction (HRI) scenarios. Unlike previous learning from demonstration (LfD) methods that cope only with the spatial features of an action, the formulated scheme effectively encompasses spatial and temporal aspects. Learned actions are reproduced under the high-level control of a time-informed task planner. During the implementation of the studied scenarios, temporal and physical constraints may impose speed adaptations in the reproduced actions. The employed latent space representation readily supports such variations, giving rise to novel actions in the temporal domain. Experimental results demonstrate the effectiveness of the proposed scheme in the implementation of HRI scenarios. Finally, a set of well-defined evaluation metrics are introduced to assess the validity of the proposed approach considering the temporal and spatial consistency of the reproduced behaviors.

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A Hierarchical Behavioral Dynamic Approach for Naturally Adaptive Human-Agent Pick-and-Place Interactions

Complexity ◽

10.1155/2019/5964632 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16

Author(s):

Maurice Lamb ◽

Patrick Nalepka ◽

Rachel W. Kallen ◽

Tamara Lorenz ◽

Steven J. Harrison ◽

...

Keyword(s):

Human Robot Interaction ◽

Robot Interaction ◽

Agent Interactions ◽

Human Agent ◽

Dinner Table ◽

Pick And Place ◽

Low Dimensional ◽

High Level ◽

Near Future ◽

Simple Interaction

Interactive or collaborative pick-and-place tasks occur during all kinds of daily activities, for example, when two or more individuals pass plates, glasses, and utensils back and forth between each other when setting a dinner table or loading a dishwasher together. In the near future, participation in these collaborative pick-and-place tasks could also include robotic assistants. However, for human-machine and human-robot interactions, interactive pick-and-place tasks present a unique set of challenges. A key challenge is that high-level task-representational algorithms and preplanned action or motor programs quickly become intractable, even for simple interaction scenarios. Here we address this challenge by introducing a bioinspired behavioral dynamic model of free-flowing cooperative pick-and-place behaviors based on low-dimensional dynamical movement primitives and nonlinear action selection functions. Further, we demonstrate that this model can be successfully implemented as an artificial agent control architecture to produce effective and robust human-like behavior during human-agent interactions. Participants were unable to explicitly detect whether they were working with an artificial (model controlled) agent or another human-coactor, further illustrating the potential effectiveness of the proposed modeling approach for developing systems of robust real/embodied human-robot interaction more generally.

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