Engineering Human-Machine Teams for Trusted Collaboration

The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities.

[1]  Kjeld Schmidt,et al.  Taking CSCW seriously , 1992, Computer Supported Cooperative Work (CSCW).

[2]  Amos Azaria,et al.  How Did You Like This Ride? An Analysis of User Preferences in Ridesharing Assignments , 2020, VEHITS.

[3]  Yiannis Demiris,et al.  Collaborative Control for a Robotic Wheelchair: Evaluation of Performance, Attention, and Workload , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Nisar R. Ahmed,et al.  “Dave...I can assure you ...that it’s going to be all right ...” A Definition, Case for, and Survey of Algorithmic Assurances in Human-Autonomy Trust Relationships , 2017, ACM Comput. Surv..

[5]  Robert E. Wray,et al.  Comparing Cognitive Models and Human Behavior Models: Two Computational Tools for Expressing Human Behavior , 2007, J. Aerosp. Comput. Inf. Commun..

[6]  Dino Mandrioli,et al.  Formal model of human erroneous behavior for safety analysis in collaborative robotics , 2019, Robotics and Computer-Integrated Manufacturing.

[7]  Frans Coenen,et al.  Driving posture recognition by convolutional neural networks , 2016, IET Comput. Vis..

[8]  Michael J. North,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[9]  Wolfram Burgard,et al.  Editorial: Shared Autonomy— Learning of Joint Action and Human-Robot Collaboration , 2019, Front. Neurorobot..

[10]  Heinz Wörn,et al.  Human–Robot Cooperation Using Multi-Agent-Systems , 2001, J. Intell. Robotic Syst..

[11]  Srimathy Mohan,et al.  Scheduling part-time personnel with availability restrictions and preferences to maximize employee satisfaction , 2008, Math. Comput. Model..

[12]  Devon S. Johnson,et al.  Cognitive and affective trust in service relationships , 2005 .

[13]  Roland Siegwart,et al.  The current state and future outlook of rescue robotics , 2019, J. Field Robotics.

[14]  Sangin Park,et al.  Infrared Camera-Based Non-contact Measurement of Brain Activity From Pupillary Rhythms , 2018, Front. Physiol..

[15]  Fabrice Talla Nobibon,et al.  Robust Optimization for the Resource-Constrained Project Scheduling Problem with Duration Uncertainty , 2015 .

[16]  Chin-Teng Lin,et al.  Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[17]  David J. Klaus,et al.  Reinforcement determinants of team proficiency , 1970 .

[18]  Jorge Cortes,et al.  Coordinated Control of Multi-Robot Systems: A Survey , 2017 .

[19]  Mahadev Satyanarayanan,et al.  Pervasive computing: vision and challenges , 2001, IEEE Wirel. Commun..

[20]  Theresa Law.  Measuring Relational Trust in Human-Robot Interactions , 2020, HRI.

[21]  Scott E. Kerick,et al.  Brain–Computer Interface Technologies in the Coming Decades , 2012, Proceedings of the IEEE.

[22]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[23]  Erik Schaffernicht,et al.  Enabling Flow Awareness for Mobile Robots in Partially Observable Environments , 2017, IEEE Robotics and Automation Letters.

[24]  Joseph A. O'Sullivan,et al.  Laser Doppler Vibrometry Measures of Physiological Function: Evaluation of Biometric Capabilities , 2010, IEEE Transactions on Information Forensics and Security.

[25]  Kazuhiro Kosuge,et al.  Progress and prospects of the human–robot collaboration , 2017, Autonomous Robots.

[26]  Gamini Dissanayake,et al.  Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm , 2011, IEEE Transactions on Biomedical Engineering.

[27]  Sandra Hirche,et al.  Considering Human Behavior Uncertainty and Disagreements in Human–Robot Cooperative Manipulation , 2017 .

[28]  William H. Sanders,et al.  The Multiple-Asymmetric-Utility System Model: A Framework for Modeling Cyber-Human Systems , 2011, 2011 Eighth International Conference on Quantitative Evaluation of SysTems.

[29]  Kai Nagel,et al.  The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data , 2019, ANT/EDI40.

[30]  Carlos Alós-Ferrer,et al.  Trust Games and Beyond , 2019, Front. Neurosci..

[31]  Jong-Soo Lee,et al.  Driver’s eye blinking detection using novel color and texture segmentation algorithms , 2012 .

[32]  Jérémie Gallien,et al.  A Smart Market for Industrial Procurement with Capacity Constraints , 2005, Manag. Sci..

[33]  Dariu Gavrila,et al.  Context-Based Path Prediction for Targets with Switching Dynamics , 2018, International Journal of Computer Vision.

[34]  Noa Agmon,et al.  Intelligent agent supporting human-multi-robot team collaboration , 2015, Artif. Intell..

[35]  M. Grigioni,et al.  Optical Vibrocardiography: A Novel Tool for the Optical Monitoring of Cardiac Activity , 2006, Annals of Biomedical Engineering.

[36]  Linet Özdamar,et al.  Uncertainty Modelling in Software Development Projects (With Case Study) , 2001, Ann. Oper. Res..

[37]  David L. Strayer,et al.  On Working Memory and a Productivity Illusion in Distracted Driving , 2016 .

[38]  Fabian Scheipl,et al.  Generalized Functional Additive Mixed Models , 2015, 1506.05384.

[39]  Robert Bogue,et al.  Robots in recycling and disassembly , 2019, Ind. Robot.

[40]  Julie A. Shah,et al.  Computational design of mixed-initiative human–robot teaming that considers human factors: situational awareness, workload, and workflow preferences , 2017, Int. J. Robotics Res..

[41]  Jorge Sá Silva,et al.  A Survey on Human-in-the-Loop Applications Towards an Internet of All , 2015, IEEE Communications Surveys & Tutorials.

[42]  Angelo Cangelosi,et al.  Would a robot trust you? Developmental robotics model of trust and theory of mind , 2019, Philosophical Transactions of the Royal Society B.

[43]  Karen Twomey,et al.  Swallowable-Capsule Technology , 2008, IEEE Pervasive Computing.

[44]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004 .

[45]  N Moray,et al.  Trust, control strategies and allocation of function in human-machine systems. , 1992, Ergonomics.

[46]  D. Gefen,et al.  Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services , 2004 .

[47]  Daniel J. McAllister.  Affect- and Cognition-Based Trust as Foundations for Interpersonal Cooperation in Organizations , 1995 .

[48]  Mariette Awad,et al.  Cooperative Heterogeneous Multi-Robot Systems , 2019, ACM Comput. Surv..

[49]  Athanasios V. Vasilakos,et al.  Body Area Networks: A Survey , 2010, Mob. Networks Appl..

[50]  Colin Camerer,et al.  Not So Different After All: A Cross-Discipline View Of Trust , 1998 .

[51]  Eduardo Salas,et al.  Principles for Measuring Teamwork Skills , 1992 .

[52]  Peter Vortisch,et al.  Creating an integrated agent-based travel demand model by combining mobiTopp and MATSim , 2019, ANT/EDI40.

[53]  Alan F. T. Winfield,et al.  Experiments in Artificial Theory of Mind: From Safety to Story-Telling , 2018, Front. Robot. AI.

[54]  Murray Shanahan,et al.  Perception as Abduction: Turning Sensor Data Into Meaningful Representation , 2005, Cogn. Sci..

[55]  Ian D. Walker,et al.  Robot-Human Handovers Based on Trust , 2015, 2015 Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI).

[56]  Dino Mandrioli,et al.  SAFER-HRC: Safety Analysis Through Formal vERification in Human-Robot Collaboration , 2016, SAFECOMP.

[57]  Robert X. Gao,et al.  Recurrent neural network for motion trajectory prediction in human-robot collaborative assembly , 2020 .

[58]  Federica Paganelli,et al.  An Evaluation of Context-Aware Infomobility Systems , 2009, Context-Aware Mobile and Ubiquitous Computing for Enhanced Usability.

[59]  Kristof Van Laerhoven,et al.  Spine versus porcupine: a study in distributed wearable activity recognition , 2004, Eighth International Symposium on Wearable Computers.

[60]  Bing Cai Kok,et al.  Trust in Robots: Challenges and Opportunities , 2020, Current Robotics Reports.

[61]  Robert Bogue,et al.  Robots in healthcare , 2011, Ind. Robot.

[62]  Jörg P. Müller,et al.  Cross-provider Platoons for Same-day Delivery , 2019, VEHITS.

[63]  John A. Stankovic,et al.  Research Directions for the Internet of Things , 2014, IEEE Internet of Things Journal.

[64]  Matthias Althoff,et al.  Calculating human reachable occupancy for guaranteed collision-free planning , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[65]  Michael P. Wellman,et al.  Auction Protocols for Decentralized Scheduling , 2001, Games Econ. Behav..

[66]  David R. Lamas,et al.  Modelling Trust: An Empirical Assessment , 2017, INTERACT.

[67]  Haiying Li,et al.  Social force models for pedestrian traffic – state of the art , 2018 .

[68]  Steven Reece,et al.  Human–agent collaboration for disaster response , 2015, Autonomous Agents and Multi-Agent Systems.

[69]  Jörg P. Müller,et al.  Zone-Specific Interaction Modeling of Pedestrians and Cars in Shared Spaces , 2020 .

[70]  J. Lewis,et al.  Trust as a Social Reality , 1985 .

[71]  Mohan M. Trivedi,et al.  Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness , 2018, IEEE Transactions on Intelligent Vehicles.

[72]  Klaus Neumann,et al.  Project scheduling with inventory constraints , 2003, Math. Methods Oper. Res..

[73]  Yue Wang,et al.  Trust-Based Optimal Subtask Allocation and Model Predictive Control for Human-Robot Collaborative Assembly in Manufacturing , 2015, HRI 2015.

[74]  Patrizia Beraldi,et al.  The Stochastic Resource-Constrained Project Scheduling Problem , 2015 .

[75]  Alexander Verl,et al.  Cooperation of human and machines in assembly lines , 2009 .

[76]  Malte F. Jung.  Affective Grounding in Human-Robot Interaction , 2017, 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI.

[77]  Praveen Damacharla,et al.  Common Metrics to Benchmark Human-Machine Teams (HMT): A Review , 2020, ArXiv.

[78]  Sorin Grigorescu,et al.  A Survey of Deep Learning Techniques for Autonomous Driving , 2020, J. Field Robotics.

[79]  Sönke Hartmann,et al.  A survey of variants and extensions of the resource-constrained project scheduling problem , 2010, Eur. J. Oper. Res..

[80]  Lorenza Manenti,et al.  An Agent-Based Pedestrian and Group Dynamics Model Applied to Experimental and Real-World Scenarios , 2015, J. Intell. Transp. Syst..

[81]  Huosheng Hu,et al.  Coordination in multi-agent RoboCup teams , 2001, Robotics Auton. Syst..

[82]  Alan R. Wagner,et al.  Modeling the Human-Robot Trust Phenomenon , 2018, ACM Trans. Interact. Intell. Syst..

[83]  Duane T. McRuer,et al.  A Review of Quasi-Linear Pilot Models , 1967 .

[84]  Jörg Homberger,et al.  Decentralized Multi-Project Scheduling , 2015 .

[85]  Jean-Philippe Thiran,et al.  Action Units and Their Cross-Correlations for Prediction of Cognitive Load during Driving , 2017, IEEE Transactions on Affective Computing.

[86]  Albrecht Schmidt,et al.  There is more to context than location , 1999, Comput. Graph..

[87]  Silvio Savarese,et al.  Long-term path prediction in urban scenarios using circular distributions , 2018, Image Vis. Comput..

[88]  Damien Trentesaux,et al.  Maintenance of the Autonomous Train: A Human-Machine Cooperation Framework , 2020 .

[89]  Yuka Kato,et al.  A pedestrian model in human-robot coexisting environment for mobile robot navigation , 2017, 2017 IEEE/SICE International Symposium on System Integration (SII).

[90]  Andreas Rausch,et al.  Toward a Holistic Software Systems Engineering Approach for Dependable Autonomous Systems , 2018, 2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS).

[91]  Jessie Y. C. Chen,et al.  A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction , 2011, Hum. Factors.

[92]  Jörg Homberger,et al.  A (μ, λ)-coordination mechanism for agent-based multi-project scheduling , 2009, OR Spectrum.

[93]  Mohan S. Kankanhalli,et al.  Interact as You Intend: Intention-Driven Human-Object Interaction Detection , 2018, IEEE Transactions on Multimedia.

[94]  Rolf H. Möhring,et al.  Scheduling under uncertainty: Optimizing against a randomizing adversary , 2000, APPROX.

[95]  G. Bente,et al.  Avatar-Mediated Networking: Increasing Social Presence and Interpersonal Trust in Net-Based Collaborations , 2008 .

[96]  Houshang Darabi,et al.  LSTM Fully Convolutional Networks for Time Series Classification , 2017, IEEE Access.

[97]  G. Borghini,et al.  Neuroscience and Biobehavioral Reviews , 2022 .

[98]  Xiaoping Zheng,et al.  A review of cellular automata models for crowd evacuation , 2019, Physica A: Statistical Mechanics and its Applications.

[99]  Helle Sørensen,et al.  An introduction with medical applications to functional data analysis , 2013, Statistics in medicine.

[100]  Yue Wang,et al.  Trust-based compliant robot-human handovers of payloads in collaborative assembly in flexible manufacturing , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

[101]  Hongliang Guo,et al.  Survey on Artificial Intelligence for Vehicles , 2018 .

[102]  Nicole Immorlica,et al.  Coordination mechanisms for selfish scheduling , 2005, Theor. Comput. Sci..