Building Strong Semi-Autonomous Systems

The vision of populating the world with autonomous systems that reduce human labor and improve safety is gradually becoming a reality. Autonomous systems have changed the way space exploration is conducted and are beginning to transform everyday life with a range of household products. In many areas, however, there are considerable barriers to the deployment of fully autonomous systems. We refer to systems that require some degree of human intervention in order to complete a task as semi-autonomous systems. We examine the broad rationale for semi-autonomy and define basic properties of such systems. Accounting for the human in the loop presents a considerable challenge for current planning techniques. We examine various design choices in the development of semi-autonomous systems and their implications on planning and execution. Finally, we discuss fruitful research directions for advancing the science of semi-autonomy.

[1]  Richard J. Doyle.  Autonomy Needs and Trends in Deep Space Exploration , 2003 .

[2]  Shlomo Zilberstein,et al.  Policy Iteration for Decentralized Control of Markov Decision Processes , 2009, J. Artif. Intell. Res..

[3]  Takayuki Kanda,et al.  A semi-autonomous communication robot — A field trial at a train station , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[4]  C.R. Jung,et al.  A lane departure warning system based on a linear-parabolic lane model , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[5]  Stephanie Rosenthal,et al.  Acquiring Accurate Human Responses to Robots’ Questions , 2012, Int. J. Soc. Robotics.

[6]  Stephen R. Marsland,et al.  Learning to select distinctive landmarks for mobile robot navigation , 2001, Robotics Auton. Syst..

[7]  John N. Tsitsiklis,et al.  On the complexity of decentralized decision making and detection problems , 1985 .

[8]  Yulan Liang,et al.  The Effects of Momentary Visual Disruption on Hazard Anticipation in Driving , 2017 .

[9]  Abdel-Illah Mouaddib,et al.  Humans-Robots Sliding Collaboration Control in Complex Environments with Adjustable Autonomy , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[10]  Shlomo Zilberstein,et al.  Plan and Activity Recognition from a Topic Modeling Perspective , 2014, ICAPS.

[11]  Debra Schreckenghost,et al.  Adjustable Autonomy for Human-Centered Autonomous Systems , 1999 .

[12]  Ari K. Jónsson,et al.  Mixed-Initiative Activity Planning for Mars Rovers , 2005, IJCAI.

[13]  A. R. Diéguez,et al.  A ROBUST STOCHASTIC SUPERVISION ARCHITECTURE FOR AN INDOOR MOBILE ROBOT , 2022 .

[14]  P. Pandurang Nayak,et al.  Remote Agent: To Boldly Go Where No AI System Has Gone Before , 1998, Artif. Intell..

[15]  Chad R. Frost.  Challenges and Opportunities for Autonomous Systems in Space , 2011 .

[16]  Nils J. Nilsson,et al.  Shakey the Robot , 1984 .

[17]  P. Kumsawat,et al.  Automatic lane detection and navigation using pattern matching mode , 2007 .

[18]  Drew McDermott,et al.  Issues in the Development of Human-Computer Mixed Initiative Planning , 1994 .

[19]  Hyunggu Jung,et al.  A user modeling approach for reasoning about interaction sensitive to bother and its application to hospital decision scenarios , 2010, User Modeling and User-Adapted Interaction.

[20]  Michael A. Goodrich,et al.  Validating human-robot interaction schemes in multitasking environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[21]  Yael Edan,et al.  Human-robot collaboration for improved target recognition of agricultural robots , 2003, Ind. Robot.

[22]  Stephanie Rosenthal,et al.  Mobile Robot Planning to Seek Help with Spatially-Situated Tasks , 2012, AAAI.

[23]  Claudia V. Goldman,et al.  Fault-Tolerant Planning under Uncertainty , 2013, IJCAI.

[24]  Shlomo Zilberstein,et al.  Multi-Objective MDPs with Conditional Lexicographic Reward Preferences , 2015, AAAI.

[25]  Shlomo Zilberstein,et al.  Formal models and algorithms for decentralized decision making under uncertainty , 2008, Autonomous Agents and Multi-Agent Systems.

[26]  Shlomo Zilberstein,et al.  Multi-Objective POMDPs with Lexicographic Reward Preferences , 2015, IJCAI.

[27]  Sarit Kraus,et al.  Collaborative Plans for Complex Group Action , 1996, Artif. Intell..

[28]  Jean Scholtz,et al.  Beyond usability evaluation: analysis of human-robot interaction at a major robotics competition , 2004 .

[29]  Michael A. Goodrich,et al.  Experiments in adjustable autonomy , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[30]  J. Marschak,et al.  Elements for a Theory of Teams , 1955 .

[31]  Paolo Traverso,et al.  Automated Planning: Theory & Practice , 2004 .

[32]  Erann Gat,et al.  Remote agent prototype for spacecraft autonomy , 1996, Optics & Photonics.

[33]  Anuj K. Pradhan,et al.  The Use of Eye Movements to Evaluate the Effects of Driver Age on Risk Perception in an Advanced Driving Simulator , 2003 .

[34]  Manuela M. Veloso,et al.  Fault Tolerant Planning: Toward Probabilistic Uncertainty Models in Symbolic Non-Deterministic Planning , 2004, ICAPS.

[35]  K. Suzuki,et al.  Development of a human and robot collaborative system for inspecting patrol of nuclear power plants , 1997, Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN'97 SENDAI.

[36]  Illah R. Nourbakhsh,et al.  A survey of socially interactive robots , 2003, Robotics Auton. Syst..

[37]  Matthew R E Romoser,et al.  Do Advance Yield Markings Increase Safe Driver Behaviors at Unsignalized, Marked Midblock Crosswalks?: Driving Simulator Study , 2011, Transportation research record.

[38]  Jeffrey M. Bradshaw,et al.  Kaa: policy-based explorations of a richer model for adjustable autonomy , 2005, AAMAS '05.

[39]  Abdel-Illah Mouaddib,et al.  Integrating Human Recommendations in the Decision Process of Autonomous Agents , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[40]  Neil Immerman,et al.  The Complexity of Decentralized Control of Markov Decision Processes , 2000, UAI.

[41]  Ronald C. Arkin,et al.  Ethical robots in warfare , 2009, IEEE Technology and Society Magazine.

[42]  Kenneth Y. Goldberg,et al.  Collaborative teleoperation via the Internet , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[43]  Sterling J. Anderson,et al.  A unified approach to semi-autonomous control of passenger vehicles in hazard avoidance scenarios , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[44]  Paolo Traverso,et al.  Automated planning - theory and practice , 2004 .

[45]  Shlomo Zilberstein,et al.  A Decision-Theoretic Approach to Cooperative Control and Adjustable Autonomy , 2010, ECAI.

[46]  Edmund H. Durfee,et al.  Blissful Ignorance: Knowing Just Enough to Coordinate Well , 1995, ICMAS.

[47]  Shlomo Zilberstein,et al.  Optimizing fixed-size stochastic controllers for POMDPs and decentralized POMDPs , 2010, Autonomous Agents and Multi-Agent Systems.

[48]  Stephanie Rosenthal,et al.  Is Someone in this Office Available to Help Me? , 2012, J. Intell. Robotic Syst..