Efficient motion intent communication is necessary for safe and collaborative work environments with collocated humans and robots. Humans efficiently communicate their motion intent to other humans through gestures, gaze, and social cues. However, robots often have difficulty efficiently communicating their motion intent to humans via these methods. Many existing methods for robot motion intent communication rely on 2D displays, which require the human to continually pause their work and check a visualization. We propose a mixed reality head-mounted display visualization of the proposed robot motion over the wearer's real-world view of the robot and its environment. To evaluate the effectiveness of this system against a 2D display visualization and against no visualization, we asked 32 participants to labeled different robot arm motions as either colliding or non-colliding with blocks on a table. We found a 16 increase in accuracy with a 62 decrease in the time it took to complete the task compared to the next best system. This demonstrates that a mixed-reality HMD allows a human to more quickly and accurately tell where the robot is going to move than the compared baselines.
A key requirement for seamless human-robot collaboration is for the robot to make its intentions clear to its human collaborator. A collaborative robot's motion must be legible, or intent-expressive. Legibility is often described in the literature as and effect of predictable, unsurprising, or expected motion. Our central insight is that predictability and legibility are fundamentally different and often contradictory properties of motion. We develop a formalism to mathematically define and distinguish predictability and legibility of motion. We formalize the two based on inferences between trajectories and goals in opposing directions, drawing the analogy to action interpretation in psychology. We then propose mathematical models for these inferences based on optimizing cost, drawing the analogy to the principle of rational action. Our experiments validate our formalism's prediction that predictability and legibility can contradict, and provide support for our models. Our findings indicate that for robots to seamlessly collaborate with humans, they must change the way they plan their motion. The animation techniques of anticipation and reaction can help create robot behaviors that are human readable such that people can figure out what the robot is doing, reasonably predict what the robot will do next, and ultimately interact with the robot in an effective way. By showing forethought before action and expressing a reaction to the task outcome (success or failure), we prototyped a set of human-robot interaction behaviors. In a 2 (forethought vs. none: between) x 2 (reaction to outcome vs. none: between) x 2 (success vs. failure task outcome: within) experiment, we tested the influences of forethought and reaction upon people's perceptions of the robot and the robot's readability. In this online video prototype experiment (N=273), we have found support for the hypothesis that perceptions of robots are influenced by robots showing forethought, the task outcome (success or failure), and showing goal-oriented reactions to those task outcomes. Implications for theory and design are discussed. Human communication involves a number of nonverbal cues that are seemingly unintentional, unconscious, and automatic-both in their production and perception-and convey rich information on the emotional state and intentions of an individual. One family of such cues is called "nonverbal leakage." In this paper, we explore whether people can read nonverbal leakage cues-particularly gaze cues-in humanlike robots and make inferences on robots' intentions, and whether the physical design of the robot affects these inferences. We designed a gaze cue for Geminoid-a highly humanlike android-and Robovie-a robot with stylized, abstract humanlike features-that allowed the robots to "leak" information on what they might have in mind. In a controlled laboratory experiment, we asked participants to play a game of guessing with either of the robots and evaluated how the gaze cue affected participants' task performance. We found that the gaze cue did, in fact, lead to better performance, from which we infer that the cue led to attributions of mental states and intentionality. Our results have implications for robot design, particularly for designing expression of intentionality, and for our understanding of how people respond to human social cues when they are enacted by robots.   When humans and mobile robots share the same space, one of their challenges is to navigate around each other and manage their mutual navigational intents. While humans have developed excellent skills in inferring their counterpart's intentions via a number of implicit and non-verbal cues, making navigation also in crowds an ease, this kind of effective and efficient communication often falls short in human-robot encounters. In this paper, two alternative approaches to convey navigational intent of a mobile robot to humans in a shared environment are proposed and analysed. The first is utilising anthropomorphic features of the mobile robot to realise an implicit joint attention using gaze to represent the direction of navigational intent. In the second approach, a more technical design adopting the semantics of car's turn indicators, has been implemented. The paper compares both approaches with each other and against a control behaviour without any communication of intent. Both approaches show statistically significant differences in comparison to the control behaviour. However, the second approach using indicators has shown as being more effective in conveying the intent and also has a higher positive impact on the comfort of the humans encountering the robot. A framework and methodology to realize robot-to-human behavioral expression is proposed. Human-robot symbiosis requires to enhance nonverbal communication between humans and robots. The proposed methodology is based on movement analysis theories of dance psychology researchers, namely Laban, Lamb and Kestenberg. Two experiments on robot-to-human behavioral expression are also presented to support the methodology. One is an experiment to produce familiarity with robot-to-human tactile reaction. The other is an experiment to express a robot's emotions by its dances. This methodology will be a key to realize robots that work close to humans cooperatively.