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Author: Abhizna Butchibabu
Publisher:
ISBN:
Category :
Languages : en
Pages : 131
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Book Description
Increasing prevalence of autonomous systems has generated interest in effective inclusion of robots as team members in many domains, especially where complex and safety-critical tasks must be performed. We envision a world where autonomous systems can be seamlessly integrated into high performing human teams. In order for team members to successfully work in concert to achieve a goal, the team must establish a common understanding of the task expectations and communicate effectively. In this dissertation , we drew inspiration from studies of effective human teamwork, which showed that best performing human teams exploit anticipatory coordination strategies (referred to as implicit coordination) to selectively communicate information based on the perceived needs of the other members in the team instead of requesting for information (referred to as explicit coordination). We elaborated upon prior characterizations of communication as implicit versus explicit by dividing implicit communication into two subtypes: (1) goal-based information (referred to as deliberative-implicit communication) and (2) status updates (referred to as reactive-implicit communication). Based on an empirical study conducted using 13 teams of 4 people working on a collaborative search-and-deliver task, we found that the best performing teams exhibited higher rates of deliberative communication than reactive communication compared to the worst-performing teams (p = 0.039). In other words, the best performing teams proactively shared goal-based information with their teammates. By gaining insight into how high-performing human teams communicate effectively, we developed a computational model using a Maximum Entropy Markov Model (MEMM) that selected the appropriate communication type (i.e., deliberative, reactive, explicit or no communication) for the autonomous agent using human teams' data. We showed that the MEMM model accuracy was high when the model was trained and tested using the best-performing teams' data (73.3%) and all 13 teams' data (92.3%) from the previously studied human-human teams. We further validated this model by assessing team performance in an empirical study where teams consisting of 2 human and 2 autonomous agent worked on a collaborative task. We compared the performance of teams with agents using the MEMM communication model to performance of teams with agents communicating using only deliberative-implicit communications or reactive-implicit communications. Results from this study showed that team performance with agents using the MEMM communication model was statistically better than team performance with agents using reactive-implicit communication model (p
Author: Abhizna Butchibabu
Publisher:
ISBN:
Category :
Languages : en
Pages : 131
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Book Description
Increasing prevalence of autonomous systems has generated interest in effective inclusion of robots as team members in many domains, especially where complex and safety-critical tasks must be performed. We envision a world where autonomous systems can be seamlessly integrated into high performing human teams. In order for team members to successfully work in concert to achieve a goal, the team must establish a common understanding of the task expectations and communicate effectively. In this dissertation , we drew inspiration from studies of effective human teamwork, which showed that best performing human teams exploit anticipatory coordination strategies (referred to as implicit coordination) to selectively communicate information based on the perceived needs of the other members in the team instead of requesting for information (referred to as explicit coordination). We elaborated upon prior characterizations of communication as implicit versus explicit by dividing implicit communication into two subtypes: (1) goal-based information (referred to as deliberative-implicit communication) and (2) status updates (referred to as reactive-implicit communication). Based on an empirical study conducted using 13 teams of 4 people working on a collaborative search-and-deliver task, we found that the best performing teams exhibited higher rates of deliberative communication than reactive communication compared to the worst-performing teams (p = 0.039). In other words, the best performing teams proactively shared goal-based information with their teammates. By gaining insight into how high-performing human teams communicate effectively, we developed a computational model using a Maximum Entropy Markov Model (MEMM) that selected the appropriate communication type (i.e., deliberative, reactive, explicit or no communication) for the autonomous agent using human teams' data. We showed that the MEMM model accuracy was high when the model was trained and tested using the best-performing teams' data (73.3%) and all 13 teams' data (92.3%) from the previously studied human-human teams. We further validated this model by assessing team performance in an empirical study where teams consisting of 2 human and 2 autonomous agent worked on a collaborative task. We compared the performance of teams with agents using the MEMM communication model to performance of teams with agents communicating using only deliberative-implicit communications or reactive-implicit communications. Results from this study showed that team performance with agents using the MEMM communication model was statistically better than team performance with agents using reactive-implicit communication model (p
Author: Fernando De La Prieta
Publisher: Springer Nature
ISBN: 3030519996
Category : Computers
Languages : en
Pages : 422
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Book Description
This book constitutes the refereed proceedings of the workshops co-located with the 18th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2020, held in L’Aquila, Italy, in October 2020. The total of 21 full and 13 short papers presented in this volume were carefully reviewed and selected from 57 submissions. The papers in this volume stem from the following meetings: Workshop on Agent-Based Artificial Markets Computational Economics (ABAM); Workshop on Agents and Edge-AI (AgEdAI); Workshop on Character Computing (C2); Workshop on MAS for Complex Networks and Social Computation (CNSC); Workshop on Decision Support, Recommendation, and Persuasion in Artificial Intelligence (DeRePAI); Workshop on Multi-Agent Systems and Simulation (MAS&S); Workshop on Multi-agent based Applications for Energy Markets, Smart Grids and Sustainable Energy Systems (MASGES); Workshop on Smart Cities and Intelligent Agents (SCIA).
Author: Tariq Iqbal
Publisher:
ISBN:
Category :
Languages : en
Pages : 208
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Book Description
As robots become more common in our daily lives, they will be expected to interact with and work with teams of people. If a robot has an understanding of the underlying dynamics of a team, then it can recognize, anticipate, and adapt to human motion to be a more effective teammate. To enable robots to understand team dynamics, I developed a new, non-linear method to detect group synchronization, which takes multiple types of discrete, task-level events into consideration. I explored this method within the context of coordinated action and validated it by applying it to both human-only and mixed human-robot teams. The results suggest that our method is more accurate in estimating group synchronization than other methods from the literature. Building on this work, I designed a new method for robots to perceive human group behavior in real-time, anticipate future actions, and synthesize their motion accordingly. I validated this approach within a human-robot interaction scenario, where a robot successfully and contingently coordinated with people in real-time. We found that robots perform better when they have an understanding of team dynamics than they do not. Moreover, I investigated how the presence and behavior of robots affect group coordination in multi-human, multi-robot teams. The results suggested that group coordination was significantly degraded when a robot joined a human-only group, and was further degraded when a second robot joined the team and employed a different anticipation algorithm from the other robot. These findings suggest that heterogeneous behavior of robots in a multi-human group can play a major role in how group coordination dynamics change. Furthermore, I designed and implemented algorithms for robots to coordinate with people in tempo-changing environments. These algorithms leveraged a human-like understanding of temporal anticipation and adaptation during the coordination process. I validated the algorithms by applying them in a human-robot drumming scenario. The results suggest that an adaptation process alone enables a robot to achieve human-level performance. Moreover, by combining anticipatory knowledge (anticipation algorithm), along with an adaptation process, a robot can be even better than people in both uniform and single tempo-changing conditions. My research will enable robots to recognize, anticipate, and adapt to human groups. This work will help enable others in the robotics community to build more fluent and adaptable robots in the future, and provide a necessary understanding for how we design future human-robot teams.
Author: Juan F. Marulanda
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 218
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Book Description
This dissertation is centered on the study of anticipation behaviors based on Artificial Intelligence (AI) strategies as a method to improve the behavior of groups of autonomous ground robots by inferring the missing messages that get lost due to a noisy outdoor environment. These missing messages represent the information that the robots share as a group in order to stay synchronized. Also, this dissertation includes the study of an AI strategy as a method to recover corrupted phonetic messages. This research is inspired on the results of my Master thesis and expands on it to include more advanced anticipation techniques with a more complex navigation and sensors system for large environments; this research includes the use of GPS and compass for navigation and a (simulated) outdoor environment that is more realistic for many robotic applications and makes the inputs to the navigation system of the robot much noisier and more complex. This research also uses a small fleet of social robots whose collective behavior is less predictable and more complex. These issues make accurate communication between the robots a key and important feature and increase the need for anticipation as an aid to recover from faulty messages and events. A faulty event occurs when the communication between the robots gets interrupted and a message does not reach its destination. In addition, this work expands the study of anticipation from two robots (one leader and a follower) to larger groups of robots (one leader and multiple followers). In this case, the followers attempt to stay in formation while the group navigates between waypoints. This means that this group of robots must coordinate their navigation control with their formation control in a complex, simulated outdoor environment. The anticipation models presented in this research are based on Fuzzy Logic and Artificial Neural Networks models (ANN). The last one was trained using methods like Backpropagation and a Genetic Algorithm. We also designed the anticipation models based on two structures that are commonly used in System Identification Theory: AutoRegressive with eXogeneous input (ARX) and AutoRegressive Moving Average with eXogenous input (ARMAX). This research includes tests of how well anticipation works to improve coordination in complex environments. Also, we introduced an additional approach to message error correction based on syntax and phonetic inference as a complementary tool to message anticipation. This is based on the idea that some messages are not lost completely but their content can get corrupted. This approach uses an inference-based approach by implementing fuzzy logic theory in order to fully recover these corrupted messages. To test this idea, an approach based on human-robot interaction through voice commands was implemented. Here, external noise or even a user with a strong accent can confuse a speech engine and produce bad data. We define this as corrupted data and we use our solution to fix it.The results showed that the ARMAX models were more successful in reducing the distance error between both a pair of robots and a larger group of robots than the ARX models while the Leader Robot run missions included in the training data. On the other hand, the ARX models were more successful than the ARMAX models in developing a generalization behavior while the Leader Robot run missions that were not similar to the training data. Also, we observed that the ANN models that were trained with Genetic Algorithms had better results than the ANN models trained with Backpropagation. We also noticed that the robots were able to recover their formation from collisions that happened during their path. But they still can only take a certain amount of collisions before running out of time to reorganize themselves. In addition, the results from the speech experiments showed that our approach was successful in recovering corrupted messages created by a speech engine after generating homophone words in a voice command.
Author: Ken Goldberg
Publisher: Springer Nature
ISBN: 3030430898
Category : Technology & Engineering
Languages : en
Pages : 931
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Book Description
This book presents the outcomes of the 12th International Workshop on the Algorithmic Foundations of Robotics (WAFR 2016). WAFR is a prestigious, single-track, biennial international meeting devoted to recent advances in algorithmic problems in robotics. Robot algorithms are an important building block of robotic systems and are used to process inputs from users and sensors, perceive and build models of the environment, plan low-level motions and high-level tasks, control robotic actuators, and coordinate actions across multiple systems. However, developing and analyzing these algorithms raises complex challenges, both theoretical and practical. Advances in the algorithmic foundations of robotics have applications to manufacturing, medicine, distributed robotics, human–robot interaction, intelligent prosthetics, computer animation, computational biology, and many other areas. The 2016 edition of WAFR went back to its roots and was held in San Francisco, California – the city where the very first WAFR was held in 1994. Organized by Pieter Abbeel, Kostas Bekris, Ken Goldberg, and Lauren Miller, WAFR 2016 featured keynote talks by John Canny on “A Guided Tour of Computer Vision, Robotics, Algebra, and HCI,” Erik Demaine on “Replicators, Transformers, and Robot Swarms: Science Fiction through Geometric Algorithms,” Dan Halperin on “From Piano Movers to Piano Printers: Computing and Using Minkowski Sums,” and by Lydia Kavraki on “20 Years of Sampling Robot Motion.” Furthermore, it included an Open Problems Session organized by Ron Alterovitz, Florian Pokorny, and Jur van den Berg. There were 58 paper presentations during the three-day event. The organizers would like to thank the authors for their work and contributions, the reviewers for ensuring the high quality of the meeting, the WAFR Steering Committee led by Nancy Amato as well as WAFR’s fiscal sponsor, the International Federation of Robotics Research (IFRR), led by Oussama Khatib and Henrik Christensen. WAFR 2016 was an enjoyable and memorable event.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 205
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Book Description
Technological advancements are enabling robots to begin working together with humans as partners on physical tasks. To design these collaborative robots to work effectively with their human counterparts, we must first understand what expectations and perceptions people have of these robots. With this understanding, we can then design collaborative behaviors that meet those needs, enabling collaborative robots to be more effective partners. In this dissertation, I aim to address questions that will allow us to design more effective collaborative robots. For example, what do interactions look like when a collaborative robot is introduced into a human environment? How do human partners perceive this robot? What collaborative behaviors would be useful for these collaborations? For those behaviors we would like to design, can we build models by hand from human-human data? From these models, can we make recommendations for how collaborative robots should employ these behaviors? This dissertation seeks to answer these questions through four studies. The first study examines three manufacturing sites that have adopted collaborative robots in their workflow, using interviews and observations to assess the current status of collaborative robots and provide recommendations about future designs. The remaining three studies, inspired by scenarios similar to the one in the first study, each focus on a specific behavioral cue: speech patterns, teaching and repair, and deictic gestures. Those studies which focus on a specific behavioral cue use human-human data to inform models of behavior that can then be implemented on a robot and tested in a human-robot evaluation, examining the impact of the model on multiple task outcomes. The contributions of this work are an understanding of real-world collaborative behaviors, conceptual models of human collaborative behaviors, a contextualization of these behaviors and an understanding of their role in facilitating interactions, and tools to facilitate developing and testing human-robot collaborations. These contributions help to inform the design and implementation of future iterations of collaborative robots.
Author: Silvan Steiner
Publisher: Frontiers Media SA
ISBN: 2889453499
Category :
Languages : en
Pages : 138
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Book Description
In many areas of human life, people perform in teams. These teams’ performances depend, at least partly, on team members’ abilities to coordinate their contributions effectively. This includes the making of decisions and the regulation of behavior in reference to the framework provided by the social group- and task-context. Given the high relevance of a deepened and integrated understanding about the mechanisms underlying coordinated team behavior, the aim of this research topic is to provide a platform for different theoretical and methodological approaches to researching and understanding coordinated team behavior in different task contexts. The articles published in this edition offer a multifaceted insight into current work on the topic.
Author: Sakae Yamamoto
Publisher: Springer
ISBN: 3030226603
Category : Computers
Languages : en
Pages : 666
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Book Description
This two-volume set LNCS 11569 and 11570 constitutes the refereed proceedings of the Thematic Area on Human Interface and the Management of Information, HIMI 2019, held as part of HCI International 2019 in Orlando, FL, USA. HCII 2019 received a total of 5029 submissions, of which 1275 papers and 209 posters were accepted for publication after a careful reviewing process. The 91 papers presented in the two volumes were organized in topical sections named: Visual information; Data visualization and analytics; Information, cognition and learning; Information, empathy and persuasion; Knowledge management and sharing; Haptic and tactile interaction; Information in virtual and augmented reality; Machine learning and intelligent systems; Human motion and expression recognition and tracking; Medicine, healthcare and quality of life applications.
Author: S.H. Kim
Publisher: Springer Science & Business Media
ISBN: 9401110166
Category : Computers
Languages : en
Pages : 194
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Book Description
Intelligent systems of the natural kind are adaptive and robust: they learn over time and degrade gracefully under stress. If artificial systems are to display a similar level of sophistication, an organizing framework and operating principles are required to manage the resulting complexity of design and behavior. This book presents a general framework for adaptive systems. The utility of the comprehensive framework is demonstrated by tailoring it to particular models of computational learning, ranging from neural networks to declarative logic. The key to robustness lies in distributed decision making. An exemplar of this strategy is the neural network in both its biological and synthetic forms. In a neural network, the knowledge is encoded in the collection of cells and their linkages, rather than in any single component. Distributed decision making is even more apparent in the case of independent agents. For a population of autonomous agents, their proper coordination may well be more instrumental for attaining their objectives than are their individual capabilities. This book probes the problems and opportunities arising from autonomous agents acting individually and collectively. Following the general framework for learning systems and its application to neural networks, the coordination of independent agents through game theory is explored. Finally, the utility of game theory for artificial agents is revealed through a case study in robotic coordination. Given the universality of the subjects -- learning behavior and coordinative strategies in uncertain environments -- this book will be of interest to students and researchers in various disciplines, ranging from all areas of engineering to the computing disciplines; from the life sciences to the physical sciences; and from the management arts to social studies.
Author: Don Harris
Publisher: Springer Nature
ISBN: 3030491838
Category : Computers
Languages : en
Pages : 479
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Book Description
This book constitutes the proceedings of the 17th International Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2020, held as part of the 22nd International Conference, HCI International 2020, which took place in Copenhagen, Denmark, in July 2020. The total of 1439 papers and 238 posters included in the 37 HCII 2020 proceedings volumes was carefully reviewed and selected from 6326 submissions. EPCE 2020 includes a total of 60 regular papers; they were organized in topical sections named: mental workload and performance; human physiology, human energy and cognition; cognition and design of complex and safety critical systems; human factors in human autonomy teaming and intelligent systems; cognitive psychology in aviation and automotive. As a result of the Danish Government's announcement, dated April 21, 2020, to ban all large events (above 500 participants) until September 1, 2020, the HCII 2020 conference was held virtually.
