Human–robot interaction

Human–robot interaction is the study of interactions between humans and robots. It is often referred as HRI by researchers. Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language understanding, design, and social sciences.


Human–robot interaction has been a topic of both science fiction and academic speculation even before any robots existed. Because HRI depends on a knowledge of (sometimes natural) human communication, many aspects of HRI are continuations of human communications topics that are much older than robotics.

The origin of HRI as a discrete problem was stated by 20th-century author Isaac Asimov in 1941, in his novel I, Robot. He states the Three Laws of Robotics as,

These three laws of robotics determine the idea of safe interaction. The closer the human and the robot get and the more intricate the relationship becomes, the more the risk of a human being injured rises. Nowadays in advanced societies, manufacturers employing robots solve this issue by not letting humans and robots share the workspace at any time. This is achieved by defining safe zones using lidar sensors or physical cages. Thus the presence of humans is completely forbidden in the robot workspace while it is working.

With the advances of artificial intelligence, the autonomous robots could eventually have more proactive behaviors, planning their motion in complex unknown environments. These new capabilities keep safety as the primary issue and efficiency as secondary. To allow this new generation of robot, research is being conducted on human detection, motion planning, scene reconstruction, intelligent behavior through task planning and compliant behavior using force control (impedance or admittance control schemes).

The goal of HRI research is to define models of humans' expectations regarding robot interaction to guide robot design and algorithmic development that would allow more natural and effective interaction between humans and robots. Research ranges from how humans work with remote, tele-operated unmanned vehicles to peer-to-peer collaboration with anthropomorphic robots.

Many in the field of HRI study how humans collaborate and interact and use those studies to motivate how robots should interact with humans.

The goal of friendly human–robot interactions

Robots are artificial agents with capacities of perception and action in the physical world often referred by researchers as workspace. Their use has been generalized in factories but nowadays they tend to be found in the most technologically advanced societies in such critical domains as search and rescue, military battle, mine and bomb detection, scientific exploration, law enforcement, entertainment and hospital care.

These new domains of applications imply a closer interaction with the user. The concept of closeness is to be taken in its full meaning, robots and humans share the workspace but also share goals in terms of task achievement. This close interaction needs new theoretical models, on one hand for the robotics scientists who work to improve the robots utility and on the other hand to evaluate the risks and benefits of this new "friend" for our modern society.

With the advance in AI, the research is focusing on one part towards the safest physical interaction but also on a socially correct interaction, dependent on cultural criteria. The goal is to build an intuitive, and easy communication with the robot through speech, gestures, and facial expressions.

Dautenhahn refers to friendly Human–robot interaction as "Robotiquette" defining it as the "social rules for robot behaviour (a ‘robotiquette’) that is comfortable and acceptable to humans"[1] The robot has to adapt itself to our way of expressing desires and orders and not the contrary. But every day environments such as homes have much more complex social rules than those implied by factories or even military environments. Thus, the robot needs perceiving and understanding capacities to build dynamic models of its surroundings. It needs to categorize objects, recognize and locate humans and further recognize their emotions. The need for dynamic capacities pushes forward every sub-field of robotics.

Furthermore, by understanding and perceiving social cues, robots can enable collaborative scenarios with humans. For example, with the rapid rise of personal fabrication machines such as desktop 3d printers, laser cutters, etc., entering our homes, scenarios may arise where robots can collaboratively share control, co-ordinate and achieve tasks together. Industrial robots have already been integrated into industrial assembly lines and are collaboratively working with humans. The social impact of such robots have been studied [2] and has indicated that workers still treat robots and social entities, rely on social cues to understand and work together.

On the other end of HRI research the cognitive modelling of the "relationship" between human and the robots benefits the psychologists and robotic researchers the user study are often of interests on both sides. This research endeavours part of human society. For effective human – humanoid robot interaction[3] numerous communication skills[4] and related features should be implemented in the design of such artificial agents/systems.

General HRI research

HRI research spans a wide range of fields, some general to the nature of HRI.

Methods for perceiving humans

Methods for perceiving humans in the environment are based on sensor information. Research on sensing components and software led by Microsoft provide useful results for extracting the human kinematics (see Kinect). An example of older technique is to use colour information for example the fact that for light skinned people the hands are lighter than the clothes worn. In any case a human modelled a priori can then be fitted to the sensor data. The robot builds or has (depending on the level of autonomy the robot has) a 3D mapping of its surroundings to which is assigned the humans locations.

Most methods intend to build a 3D model through vision of the environment. The proprioception sensors permit the robot to have information over its own state. This information is relative to a reference.

A speech recognition system is used to interpret human desires or commands. By combining the information inferred by proprioception, sensor and speech the human position and state (standing, seated). In this matter, Natural language processing is concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. For instance, neural network architectures and learning algorithms that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling.[5]

Methods for motion planning

Motion planning in dynamic environment is a challenge that is for the moment only achieved for 3 to 10 degrees of freedom robots. Humanoid robots or even 2 armed robots that can have up to 40 degrees of freedom are unsuited for dynamic environments with today's technology. However lower-dimensional robots can use potential field method to compute trajectories avoiding collisions with human.

Cognitive models and theory of mind

Humans exhibit negative social and emotional responses as well as decreased trust toward some robots that closely, but imperfectly, resemble humans; this phenomenon has been termed the "Uncanny Valley."[6] However recent research in telepresence robots has established that mimicking human body postures and expressive gestures has made the robots likeable and engaging in a remote setting.[7] Further, the presence of a human operator was felt more strongly when tested with an android or humanoid telepresence robot than with normal video communication through a monitor.[8]

While there is a growing body of research about users' perceptions and emotions towards robots, we are still far from a complete understanding. Only additional experiments will determine a more precise model.

Based on past research, we have some indications about current user sentiment and behavior around robots:[9][10]

  • During initial interactions, people are more uncertain, anticipate less social presence, and have fewer positive feelings when thinking about interacting with robots, and prefer to communicate with a human. This finding has been called the human-to-human interaction script.
  • It has been observed that when the robot performs a proactive behaviour and does not respect a "safety distance" (by penetrating the user space) the user sometimes expresses fear. This fear response is person-dependent.
  • It has also been shown that when a robot has no particular use, negative feelings are often expressed. The robot is perceived as useless and its presence becomes annoying.
  • People have also been shown to attribute personality characteristics to the robot that were not implemented in software.

Methods for human-robot coordination

A large body of work in the field of human-robot interaction has looked at how humans and robots may better collaborate. The primary social cue for humans while collaborating is the shared perception of an activity, to this end researchers have investigated anticipatory robot control through various methods including: monitoring the behaviors of human partners using eye tracking, making inferences about human task intent, and proactive action on the part of the robot.[11] The studies revealed that the anticipatory control helped users perform tasks faster than with reactive control alone.

A common approach to program social cues into robots is to first study human-human behaviors and then transfer the learning. For example, coordination mechanisms in human-robot collaboration[12] are based on work in neuroscience[13] which examined how to enable joint action in human-human configuration by studying perception and action in a social context rather than in isolation. These studies have revealed that maintaining a shared representation of the task is crucial for accomplishing tasks in groups. For example, the authors have examined the task of driving together by separating responsibilities of acceleration and braking i.e., one person is responsible for accelerating and the other for braking; the study revealed that pairs reached the same level of performance as individuals only when they received feedback about the timing of each other's actions. Similarly, researchers have studied the aspect of human-human handovers with household scenarios like passing dining plates in order to enable an adaptive control of the same in human-robot handovers.[14] Most recently, researchers have studied a system that automatically distributes assembly tasks among co-located workers to improve co-ordination.[15]

Application Areas

The application areas of human-robot interaction include robotic technologies that are used by humans for industry, medicine, and companionship, among other purposes.

Industrial Robots

Industrial robots have been implemented to collaborate with humans to perform industrial manufacturing tasks. While humans have the flexibility and the intelligence to consider different approaches to solve the problem, choose the best option among all choices, and then command robots to perform assigned tasks, robots are able to be more precise and more consistent in performing repetitive and dangerous work.[16] Together, the collaboration of industrial robots and humans demonstrates that robots have the capabilities to ensure efficiency of manufacturing and assembling.[16] However, there are persistent concerns about the safety of human-robot collaboration, since industrial robots have the ability to move heavy objects and operate often dangerous and sharp tools, quickly and with force. As a result, this presents a potential threat to the people who work in the same workspace.[16]

Medical Robots


A rehabilitation robot is an example of a robot-aided system implemented in health care. This type of robot would aid stroke survivors or individuals with neurological impairment to recover their hand and finger movements.[17][18] In the past few decades, the idea of how human and robot interact with each other is one factor that has been widely considered in the design of rehabilitation robots.[18] For instance, human-robot interaction plays an important role in designing exoskeleton rehabilitation robots since the exoskeleton system makes direct contact with humans’ body.[17]

Elder Care and Companion Robot

Nursing robots are aimed to provide assistance to elderly people who may have faced a decline in physical and cognitive function, and, consequently, developed psychosocial issues.[19] By assisting in daily physical activities, physical assistance from the robots would allow the elderly to have a sense of autonomy and feel that they are still able to take care of themselves and stay in their own homes.[19]

Social Robots

Autism Intervention

Over the past decade, human-robot interaction has shown promising outcomes in autism intervention.[21] Children with autism spectrum disorders (ASD) are more likely to connect with robots than humans, and using social robots is considered to be a beneficial approach to help these children with ASD.[21] However, social robots that are used to intervene in children's ASD are not viewed as viable treatment by clinical communities because the study of using social robots in ASD intervention, often, does not follow standard research protocol.[21] In addition, the outcome of the research could not demonstrate a consistent positive effect that could be considered as evidence-based practice (EBP) based on the clinical systematic evaluation.[21] As a result, the researchers have started to establish guidelines which suggest how to conduct studies with robot-mediated intervention and hence produce reliable data that could be treated as EBP that would allow clinicians to choose to use robots in ASD intervention.[21]

Automatic Driving

A specific example of human-robot interaction is the human-vehicle interaction in automated driving. The goal of human-vehicle cooperation is to ensure safety, security, and comfort in automated driving systems.[22] The continued improvement in this system and the progress in advancements towards highly and fully automated vehicles aim to make the driving experience safer and more efficient in which humans do not need to intervene in the driving process when there is an unexpected driving condition such as a pedestrian walking across the street when it is not supposed to.[22]

Search and Rescue

Unmanned Aerial Vehicles (UAV) and Unmanned Underwater Vehicles (UUV) have the potential to assist search and rescue work in wilderness areas, such as locating a missing person remotely from the evidence that they left in surrounding areas.[23][24] The system integrates autonomy and information, such as coverage maps, GPS information and quality search video, to support humans performing the search and rescue work efficiently in the given limited time.[23][24]

Space Exploration

Humans have been working on achieving the next breakthrough in space exploration, such as a manned mission to Mars.[25] This challenge identified the need for developing planetary rovers that are able to assist astronauts and support their operations during their mission.[25] The collaboration between rovers, unmanned aerial vehicles, and humans enables leveraging capabilities from all sides and optimizes task performance.[25]

See also





Bartneck and Okada[26] suggest that a robotic user interface can be described by the following four properties:

Tool – toy scale
  • Is the system designed to solve a problem effectively or is it just for entertainment?
Remote control – autonomous scale
  • Does the robot require remote control or is it capable of action without direct human influence?
Reactive – dialogue scale
  • Does the robot rely on a fixed interaction pattern or is it able to have dialogue — exchange of information — with a human?
Anthropomorphism scale
  • Does it have the shape or properties of a human?


International Conference on Social Robotics

The International Conference on Social Robotics is a conference for scientists, researchers, and practitioners to report and discuss the latest progress of their forefront research and findings in social robotics, as well as interactions with human beings and integration into our society.

  • ICSR2009, Incheon, Korea in collaboration with the FIRA RoboWorld Congress
  • ICSR2010, Singapore
  • ICSR2011, Amsterdam, Netherlands

International Conference on Human-Robot Personal Relationships

  • HRPR2008, Maastricht
  • HRPR 2009, Tilburg. Keynote speaker was Hiroshi Ishiguro.
  • HRPR2010, Leiden. Keynote speaker was Kerstin Dautenhahn.

International Symposium on New Frontiers in Human-Robot Interaction

This symposium is organized in collaboration with the Annual Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour.

  • 2015, Canterbury, United Kingdom
  • 2014, London, United Kingdom
  • 2010, Leicester, United Kingdom
  • 2009, Edinburgh, United Kingdom

IEEE International Symposium in Robot and Human Interactive Communication

The IEEE International Symposium on Robot and Human Interactive Communication ( RO-MAN ) was founded in 1992 by Profs. Toshio Fukuda, Hisato Kobayashi, Hiroshi Harashima and Fumio Hara. Early workshop participants were mostly Japanese, and the first seven workshops were held in Japan. Since 1999, workshops have been held in Europe and the United States as well as Japan, and participation has been of international scope.

ACM/IEEE International Conference on Human-Robot Interaction

This conference is amongst the best conferences in the field of HRI and has a very selective reviewing process. The average acceptance rate is 26% and the average attendance is 187. Around 65% of the contributions to the conference come from the US and the high level of quality of the submissions to the conference becomes visible by the average of 10 citations that the HRI papers attracted so far.[27]

  • HRI 2006 in Salt Lake City, Utah, USA, Acceptance Rate: 0.29
  • HRI 2007 in Washington, D.C., USA, Acceptance Rate: 0.23
  • HRI 2008 in Amsterdam, Netherlands, Acceptance Rate: 0.36 (0.18 for oral presentations)
  • HRI 2009 in San Diego, CA, USA, Acceptance Rate: 0.19
  • HRI 2010 in Osaka, Japan, Acceptance Rate: 0.21
  • HRI 2011 in Lausanne, Switzerland, Acceptance Rate: 0.22 for full papers
  • HRI 2012 in Boston, Massachusetts, USA, Acceptance Rate: 0.25 for full papers
  • HRI 2013 in Tokyo, Japan, Acceptance Rate: 0.24 for full papers
  • HRI 2014 in Bielefeld, Germany, Acceptance Rate: 0.24 for full papers
  • HRI 2015 in Portland, Oregon, USA, Acceptance Rate: 0.25 for full papers
  • HRI 2016 in Christchurch, New Zealand, Acceptance Rate: 0.25 for full papers
  • HRI 2017 in Vienna, Austria, Acceptance Rate: 0.24 for full papers
  • HRI 2018 in Chicago, USA, Acceptance Rate: 0.24 for full papers

International Conference on Human-Agent Interaction

There are many conferences that are not exclusively HRI, but deal with broad aspects of HRI, and often have HRI papers presented.

  • IEEE-RAS/RSJ International Conference on Humanoid Robots (Humanoids)
  • Ubiquitous Computing (UbiComp)
  • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • Intelligent User Interfaces (IUI)
  • Computer Human Interaction (CHI)
  • American Association for Artificial Intelligence (AAAI)

There are currently two dedicated HRI Journals

  • International Journal of Social Robotics
  • The open access Journal of Human-Robot Interaction

and there are several more general journals in which one will find HRI articles.


  1. Dautenhahn, Kerstin (29 April 2007). "Socially intelligent robots: dimensions of human–robot interaction". Philosophical Transactions of the Royal Society B: Biological Sciences. 362 (1480): 679–704. doi:10.1098/rstb.2006.2004. PMC 2346526. PMID 17301026.
  2. Sauppé, Allison; Mutlu, Bilge (2015). "The Social Impact of a Robot Co-Worker in Industrial Settings". Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI '15. pp. 3613–3622. doi:10.1145/2702123.2702181. ISBN 978-1-4503-3145-6.
  3. Human-Robot Interaction.
  4. Bubaš, Goran; Lovrenčić, Alen (2002). Implications of interpersonal communication competence research on the design of artificial behavioral systems that interact with humans. Proceedings of the 6th International Conference on Intelligent Engineering Systems - INES 2002.
  5. Collobert, Ronan; Weston, Jason; Bottou, Léon; Karlen, Michael; Kavukcuoglu, Koray; Kuksa, Pavel (2011). Natural Language Processing (Almost) from Scratch. OCLC 963993063.
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  7. Adalgeirsson, Sigurdur; Breazeal, Cynthia (2010). MeBot: A Robotic Platform for Socially Embodied Presence (pdf). Hri '10. pp. 15–22. ISBN 9781424448937.
  8. Sakamoto, Daisuke; Kanda, Takayuki; Ono, Tetsuo; Ishiguro, Hiroshi; Hagita, Norihiro (2007). "Android as a telecommunication medium with a human-like presence". Proceeding of the ACM/IEEE international conference on Human-robot interaction - HRI '07. p. 193. doi:10.1145/1228716.1228743. ISBN 978-1-59593-617-2.
  9. Spence, Patric R.; Westerman, David; Edwards, Chad; Edwards, Autumn (July 2014). "Welcoming Our Robot Overlords: Initial Expectations About Interaction With a Robot". Communication Research Reports. 31 (3): 272–280. doi:10.1080/08824096.2014.924337.
  10. Edwards, Chad; Edwards, Autumn; Spence, Patric R.; Westerman, David (21 December 2015). "Initial Interaction Expectations with Robots: Testing the Human-To-Human Interaction Script". Communication Studies. 67 (2): 227–238. doi:10.1080/10510974.2015.1121899.
  11. Anticipatory Robot Control for Efficient Human-Robot Collaboration (pdf). Hri '16. 2016. pp. 83–90. ISBN 9781467383707.
  12. Coordination mechanisms in human-robot collaboration. Proceedings of the ACM/IEEE International Conference on Human-robot Interaction. 2013. CiteSeerX
  13. Sebanz, Natalie; Bekkering, Harold; Knoblich, Günther (February 2006). "Joint action: bodies and minds moving together". Trends in Cognitive Sciences. 10 (2): 70–76. doi:10.1016/j.tics.2005.12.009. PMID 16406326.
  14. Huang, Chien-Ming; Cakmak, Maya; Mutlu, Bilge (2015). Adaptive Coordination Strategies for Human-Robot Handovers (PDF). Robotics: Science and Systems.
  15. "WeBuild: Automatically Distributing Assembly Tasks Among Collocated Workers to Improve Coordination" (PDF). 2017. Cite journal requires |journal= (help)
  16. Hentout, Abdelfetah; Aouache, Mustapha; Maoudj, Abderraouf; Akli, Isma (2019-08-18). "Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017". Advanced Robotics. 33 (15–16): 764–799. doi:10.1080/01691864.2019.1636714. ISSN 0169-1864.
  17. Aggogeri, Francesco; Mikolajczyk, Tadeusz; O’Kane, James (April 2019). "Robotics for rehabilitation of hand movement in stroke survivors". Advances in Mechanical Engineering. 11 (4): 168781401984192. doi:10.1177/1687814019841921. ISSN 1687-8140.
  18. Oña, Edwin Daniel; Garcia-Haro, Juan Miguel; Jardón, Alberto; Balaguer, Carlos (2019-06-26). "Robotics in Health Care: Perspectives of Robot-Aided Interventions in Clinical Practice for Rehabilitation of Upper Limbs". Applied Sciences. 9 (13): 2586. doi:10.3390/app9132586. ISSN 2076-3417.
  19. Robinson, Hayley; MacDonald, Bruce; Broadbent, Elizabeth (November 2014). "The Role of Healthcare Robots for Older People at Home: A Review". International Journal of Social Robotics. 6 (4): 575–591. doi:10.1007/s12369-014-0242-2. ISSN 1875-4791.
  20. Curtis, Sophie (2017-07-28). "This creepy-looking humanoid robot has a very important purpose". mirror. Retrieved 2019-10-28.
  21. Begum, Momotaz; Serna, Richard W.; Yanco, Holly A. (April 2016). "Are Robots Ready to Deliver Autism Interventions? A Comprehensive Review". International Journal of Social Robotics. 8 (2): 157–181. doi:10.1007/s12369-016-0346-y. ISSN 1875-4791.
  22. Biondi, Francesco; Alvarez, Ignacio; Jeong, Kyeong-Ah (2019-07-03). "Human–Vehicle Cooperation in Automated Driving: A Multidisciplinary Review and Appraisal". International Journal of Human–Computer Interaction. 35 (11): 932–946. doi:10.1080/10447318.2018.1561792. ISSN 1044-7318.
  23. Goodrich, M. A.; Lin, L.; Morse, B. S. (May 2012). "Using camera-equipped mini-UAVS to support collaborative wilderness search and rescue teams". 2012 International Conference on Collaboration Technologies and Systems (CTS): 638. doi:10.1109/CTS.2012.6261008. ISBN 978-1-4673-1382-7.
  24. Morse, Bryan S.; Engh, Cameron H.; Goodrich, Michael A. (2010). "UAV video coverage quality maps and prioritized indexing for wilderness search and rescue". Proceeding of the 5th ACM/IEEE International Conference on Human-robot Interaction - HRI '10. Osaka, Japan: ACM Press: 227. doi:10.1145/1734454.1734548. ISBN 9781424448937.
  25. Bernard, Tiziano; Martusevich, Kirill; Rolins, Armando A.; Spence, Isaac; Troshchenko, Alexander; Chintalapati, Sunil (2018-09-17). "A Novel Mars Rover Concept for Astronaut Operational Support on Surface EVA Missions". 2018 AIAA SPACE and Astronautics Forum and Exposition. Orlando, FL: American Institute of Aeronautics and Astronautics. doi:10.2514/6.2018-5154. ISBN 9781624105753.
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  27. Bartneck, Christoph (February 2011). "The end of the beginning: a reflection on the first five years of the HRI conference". Scientometrics. 86 (2): 487–504. doi:10.1007/s11192-010-0281-x. PMC 3016230. PMID 21297856.


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