Expressing thought: Improving robot readability with animation principles

By Leila Takayama, Doug Dooley, Wendy Ju. IEEE/ACM International Conference on Human-Robot Interaction, 2011

321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Social Psychology

View Paper: https://www.semanticscholar.org/paper/8aea0f57925ef1dd23fd8c0aa0f8d3e566d38580

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Social Psychology

Citations in the corpus

A Survey of Methods for Safe Human-Robot Interaction (2017, result): “This is in contrast to other work, such as that of Vasic and Billard (2013), who partially outlined these possibilities but organized the paper according to application and focused on other aspects of safety, such as potential sources of danger and liability.”
Designing robots with movement in mind (2014, methodology): “…on completely abstract shapes, such as flat elements blocks or cardboard cutouts (Vertelney, 1995); on virtual animated models of the robots (Takayama, Dooley, & Ju, 2011); on skeletal models; or by using the final built robot, using either Wizard of Oz puppeteering techniques or autonomous…”
Effects of Robot Motion on Human-Robot Collaboration (2015, background): “Predictability and legibility are often recognized and studied as important properties of motion [1, 2, 6, 8, 10, 14, 18].”
Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation (2015, background): “Animators call this behavior “readability”, and it can be employed to create a more human like intelligence (see [106]).”
Evaluating Fluency in Human–Robot Collaboration (2019, unknown): “In addition, the robot could mitigate its perceived idle time during processing by communicating its internal processing, as suggested in a number of HRI projects [12], [13].”
A survey of robot learning from demonstrations for Human-Robot Collaboration (2017, background): “Leila Takayama et al. applied animation principles to create readable robots behaviors [29].”
Explainable Agents and Robots: Results from a Systematic Literature Review (2019, background): “Intent communication [3] [98] [85] [13] [70] [25] [92] [93] [31] [78] [17] [52]”
Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development (2018, methodology): “, 2017), intelligence (Takayama et al., 2011; Bajones et al., 2016; Ragni et al., 2016), and likeability (Bajones et al.”
Legibility and predictability of robot motion (2013, background): “This formalizes terms like “readable” [5], or “understandable” [7], and encourages “anticipatory” motion [6] because it brings the relevant information for goal prediction towards the beginning of the trajectory, thus lowering t.”
Communicating Robot Arm Motion Intent Through Mixed Reality Head-mounted Displays (2017, methodology): “n-verbal cues— especially robots that are not of human form. One alternative is to use animation and animated storytelling techniques, such as forming suggestive poses or generating initial movements [37]. This increases legibility: the ability to infer the robot’s goal through its directed motion [”
Communicating Intent of Automated Vehicles to Pedestrians (2018, background): “…1996), presence and behavior of other road users (Rosenbloom, 2009; Zhou et al., 2009), demographics of drivers and pedestrians (Oxley et al., 2005; Lobjois and Cavallo, 2007; Tom and Granié, 2011), as well as their experiences, knowledge, motivations, and cognitive state (Mwakalonge et al., 2015).”
Generating Legible Motion (2013, background): “Legible motion, sometimes referred to as readable [32] or anticipatory [18], has repeatedly been cited as essential for robots that work around humans [2, 5, 14, 23].”
Social eye gaze in human-robot interaction (2017, background): “Expressive eye gaze is one behavior (among many drawn from animation principles) that can make intentions and desires more explicit, for instance, by looking at a door handle when wanting to open a door (Takayama, Dooley, & Ju, 2011).”