Papers indicated by the solemn emoticon (-_-) are required readings. You will write a paragraph about these readings.

Papers indicated by the zen-like emoticon (^_^) are available for presentation.

Anatomy of an HRI Paper

• 
  Huang et al. (2015). Adaptive Coordination Strategies for Human-Robot Handovers. In Proc. of Robotics: Science and Systems (RSS’2015). [PDF]
• Sisbot et al. (2007). A human aware mobile robot motion planner. IEEE Transactions on Robotics 23(5):874–883. [PDF available with Cornell login]
• Fraune et al. (2015). Three’s company, or a crowd?: The effects of robot number and behavior on HRI in Japan and the USA. In Proc. of Robotics: Science and Systems (RSS’2015). [PDF]

Probability + Bayes Nets

• Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapters 13-14

Markov Models + HMMs 

• Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 15

Filtering, Kalman Filters, & Particle Filters

• Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapters 15.4  on Kalman Filters and 15.5 (specifically 15.5.3) on Particle Filters
• Thrun, Burgard, & Fox (2005). Probabilistic robotics. MIT Press. Chapters 2-4.

Intention Recognition

• Dennett (1978). Three kinds of intentional psychology. In The Intentional Stance, 43-68. MIT Press. [Distributed in Class] (-_-)
• Breazeal et al. (2009). An embodied cognition approach to mindreading skills for socially intelligent robots. The International Journal of Robotics Research, 28(5), 656-680. [PDF] (^_^) x 2
• Koppula et al.  (2013). Learning human activities and object affordances from RGB-D videos. The International Journal of Robotics Research, 32(8), 951-970. [PDF] (^_^)
• Mainprice et al. (2015). Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA 2015), pp. 885-892. [PDF] (^_^)
• Clark-Turner & Begum (2018). Deep Reinforcement Learning of Abstract Reasoning from Demonstrations. In 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI). [PDF] (^_^)

Intentional and Legible Motion

• Beetz et al. (2010). Generality and legibility in mobile manipulation. Autonomous Robots, 28(1), 21-44.  [PDF] (-_-) (^_^)
• Dragan & Srinivasa (2014). Integrating human observer inferences into robot motion planning. Autonomous Robots, 37(4), 351-368. [PDF] (^_^)
• Szafir et al. (2014). Communication of intent in assistive free flyers. In Proc. of the 2014 ACM/IEEE international conference on Human-robot interaction (HRI’14), pp. 358-365. [PDF] (^_^)
• Mainprice et al. (2010). Planning safe and legible hand-over motions for human-robot interaction. In IARP workshop on technical challenges for dependable robots in human environments. [PDF] (^_^)

Social Navigation

• Helbing & Molnar (1995). Social force model for pedestrian dynamics. Physical review E,51(5), 4282. [PDF] (-_-)
• Sisbot et al. (2007). A human aware mobile robot motion planner. IEEE Transactions on Robotics, 23(5), 874-883. [PDF available above with Cornell login] (^_^)
• Papadakis et al. (2014). Adaptive spacing in human-robot interactions. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’14), 2627–2632. [PDF] (^_^)
• Murakami et al. (2014). Destination unknown: walking side-by-side without knowing the goal. In Proceedings of the ACM/IEEE international conference on Human-robot interaction (HRI’14), 471–478. [PDF] (^_^)
• Kollmitz et al. (2015). Time dependent planning on a layered social cost map for human-aware robot navigation. In Proc. of the 2015 European Conference on Mobile Robots (ECMR), [PDF]
• Mead & Matarić  (2017). Autonomous human–robot proxemics: socially aware navigation based on interaction potential. Autonomous Robots, 41(5), 1189-1201. [PDF] (^_^)

Nonverbal Behavior: Gestures

• Ekman & Friesen (1969). The repertoire of nonverbal behavior: Categories, origins, usage, and coding. Semiotica, 1(1), 49-98. [PDF] (-_-)
• Calinon & Billard (2007). Incremental learning of gestures by imitation in a humanoid robot. In Proc. of the ACM/IEEE international conference on Human-robot interaction (HRI’07), pp. 255-262.[PDF] (^_^)
• Brooks & Arkin (2007). Behavioral overlays for non-verbal communication expression on a humanoid robot. Autonomous Robots, 22(1), 55-74. [PDF]  (^_^)
• Sigalas et al. (2009). Visual tracking of independently moving body and arms. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3005-3010). IEEE. [PDF] (^_^)
• Ou & Grupen (2010). From manipulation to communicative gesture. In Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction (HRI’10). 325–332. [PDF] (^_^)
• Dragan et al. (2015) Effects of robot motion on human-robot collaboration. In Proc. of the ACM/IEEE International Conference on Human-Robot Interaction (HRI’15), pp. 51-58. [PDF] (^_^)
• Kwon et al. (2018). Expressing Robot Incapability. In Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 87-95. [PDF] (^_^)

Nonverbal Behavior: Gaze

• Argyle et al. (1973). The different functions of gaze. Semiotica, 7(1), 19-32. [PDF] (-_-)
• Kuno et al. (2006). Museum guide robot with communicative head motion. In 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN’06), 33-38. IEEE. [PDF] (^_^)
• Mutlu et al. (2009). Nonverbal leakage in robots: Communication of intentions through seemingly unintentional behavior. 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI’09), 69-76. [PDF] (^_^)
• Andrist et al. (2014). Conversational gaze aversion for humanlike robots. In Proceedings of the  ACM/IEEE international conference on Human-robot interaction (HRI’14), 25-32. [PDF] (^_^)
• Admoni et al. (2014). Deliberate delays during robot-to-human handovers improve compliance with gaze communication. In Proc. of the 10th ACM/IEEE international conference on Human-robot interaction (HRI’14). [PDF] (^_^)
• Zaraki et al. (2014). Designing and evaluating a social gaze-control system for a humanoid robot. IEEE Transactions on Human-Machine Systems, 44(2), 157-168. [PDF available with Cornell login] (^_^)
• Argyle & Dean (1965). Eye-contact, distance and affiliation. Sociometry 28(3), 289-304. [PDF]

MDP + POMDP

• Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 17
• Thrun, Burgard, & Fox (2005). Probabilistic robotics. MIT Press. Chapters 14-16.
• Sutton & Barto (2018). Reinforcement Learning: An Introduction. MIT Press. Chapter 3.

Human-Robot Collaboration

• Bratman, M. E. (1992). Shared cooperative activity. The philosophical review, 101(2), 327-341. [PDF] (-_-)
• Hoffman & Breazeal (2004). Collaboration in Human-Robot Teams. In Proc. of the AIAA 1st Intelligent Systems Technical Conference, Chicago, IL, USA. [PDF] (^_^)
• Hoffman & Breazeal (2007). Cost-based anticipatory action selection for human–robot fluency. IEEE Transactions on Robotics, 23(5), 952-961. [PDF] (^_^)
• Shah et al. (2007). A Fast Incremental Algorithm for Maintaining Dispatchability of Partially Controllable Plans. International Conference on Automated Planning and Scheduling (ICAPS07), pp. 296-303). [PDF] (^_^)
• Grigore et al. (2018,). Predicting Supportive Behaviors for Human-Robot Collaboration. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. [PDF] (^_^)
• Hawkins, et al. (2014). Anticipating human actions for collaboration in the presence of task and sensor uncertainty. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA 2014), pp. 2215-2222 [PDF] (^_^)
• Sebanz, N., et al. (2006). Joint action: bodies and minds moving together. Trends in Cognitive Sciences, 10(2), 70-76. [PDF]
• Cohen, P. R., & Levesque, H. J. (1991). Teamwork. Nous, 25(4), 487-512. [PDF available with Cornell login]

Handovers

• Strabala et al. (2013). Towards seamless human-robot handovers. Journal of Human-Robot Interaction, 2(1), 112-132. [PDF] (-_-)
• Sisbot & Alami (2012). A human-aware manipulation planner. IEEE Transactions on Robotics, 28(5), 1045-1057. [PDF available with University login] (^_^)
•  Yamane et al. (2013). Synthesizing object receiving motions of humanoid robots with human motion database. In IEEE International Conference onRobotics and Automation (ICRA’13), 1629-1636 [PDF] (^_^)
• Shi et al. (2013). A Model of Distributional Handing Interaction for a Mobile Robot. In Robotics: Science and Systems (RSS’13). [PDF] (^_^)
• 
  Huang et al. (2015). Adaptive Coordination Strategies for Human-Robot Handovers. In Proc. of Robotics: Science and Systems (RSS’2015). [PDF]  (^_^)

Multimodal Dialog

• Clark & Tree (2002). Using uh and um in spontaneous speaking. Cognition, 84(1), 73-111. [PDF] (-_-)
• Rybski et al. (2007). Interactive robot task training through dialog and demonstration. In Proceedings of the ACM/IEEE international conference on Human-robot interaction. 49-56. [PDF] (^_^)
• Chao & Thomaz (2012). Timing in multimodal turn-taking interactions: Control and analysis using timed petri nets. Journal of Human-Robot Interaction, 1(1). [PDF] (^_^)
• Bohus & Horvitz. (2014). Managing human-robot engagement with forecasts and… um… hesitations. In Proc. of the 16th international conference on multimodal interaction. 2-9. ACM. [PDF] (^_^)

Clustering + Supervised Learning

• Russell & Norvig (2009). Artificial Intelligence: a Modern Approach (3rd ed.). Prentice Hall. Chapter 18, 20

Reinforcement Learning 

• Artificial Intelligence: a Modern Approach. Russell & Norvig, Prentice Hall (2009, 3rd ed.) chapter 21

Learning from Demonstration

• Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robotics and autonomous systems, 57(5), 469-483. [PDF] (-_-)
• Chernova, S. and Veloso, M. (2010). Confidence-based multi-robot learning from demonstration. International Journal of Social Robotics, 2(2):195–215. [PDF] (^_^)
• Akgun, B., Cakmak, M., Yoo, J. W., & Thomaz, A. L. (2012). Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective. In Proc. of the seventh annual ACM/IEEE international conference on Human-Robot Interaction (HRI’12), pp. 391-398.[PDF] (^_^)
• Niekum et al. (2012). Learning and generalization of complex tasks from unstructured demonstrations. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5239-5246. [PDF] (^_^)
• Ye G. and Alterovitz R. (2017) Demonstration-Guided Motion Planning. In: Christensen H., Khatib O. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 100. [PDF] (^_^)
• Meltzoff, A. N., & Decety, J. (2003). What imitation tells us about social cognition: a rapprochement between developmental psychology and cognitive neuroscience. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 358(1431), 491-500. [PDF]

Socially Guided Robot Learning

• Meltzoff (2007). ‘Like me’: a foundation for social cognition. Developmental science, 10(1), 126-134. [PDF] (-_-)
• Thomaz & Cakmak (2009). Learning about objects with human teachers. In Proc. of the 4th ACM/IEEE international conference on Human robot interaction (HRI’09), pp. 15-22. [PDF] (^_^)
•  Cakmak & Thomaz  (2012). Designing robot learners that ask good questions. In Proc. of the seventh annual ACM/IEEE international conference on Human-Robot Interaction (HRI’12), pp. 17-24. [PDF] (^_^)
• Griffith et al. (2013). Policy shaping: Integrating human feedback with reinforcement learning. In Advances in neural information processing systems 2625-2633. [PDF] (^_^)
• Knox et al. (2013). Training a robot via human feedback: A case study. In International Conference on Social Robotics (ICSR’13), 460-470 [PDF] (^_^)

P-Values and Confidence Intervals

• Kline (2004). Beyond significance testing: Reforming data
  analysis methods in behavioral research.  Chapter 3. Washington, DC:
  APA Books. [PDF]
• Simmons et al. (2011). False-positive psychology undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22 (11) 1359–1366. [PDF]
• Cumming (2014).  The New Statistics: Why and How. Psychological Science, 25(1) 7–29. [PDF]
• Cumming & Finch (2005). Inference by eye: confidence intervals, and how to read pictures of data.  American Psychologist, 60, 170–180. [PDF]

And that’s not all! There is an additional categorized HRI reading list for motivated students.

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