No fair!! An interaction with a cheating robot

Short, Elaine Schaertl; Hart, Justin W.; Vu, Michelle; Scassellati, Brian

doi:10.1109/hri.2010.5453193

Cited by 159 publications

(100 citation statements)

References 17 publications

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“…In reality, observers might get confused by the robot's behavior and stop reasoning about the robot's possible goals the way the model assumes they would -comparing the sub-optimality of its actions with respect to each of them. Instead, they might start believing that the robot is malfunctioning [46] or that it is not pursuing any of the goals and doing something else entirely -this is supported by our user study in Sec. X-B, which shows that this belief significantly increases at higher C costs.…”

Section: The Unpredictability Of Legibilitymentioning

confidence: 54%

Integrating human observer inferences into robot motion planning

Dragan

Srinivasa

2014

Auton Robot

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Abstract-Our goal is to enable robots to produce motion that is suitable for human-robot collaboration and coexistence. Most motion in robotics is purely functional, ideal when the robot is performing a task in isolation. In collaboration, however, the robot's motion has an observer, watching and interpreting the motion.In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator.We formalize predictability and legibility as properties of motion that naturally arise from the inferences in opposing directions that the observer makes, drawing on action interpretation theory in psychology. We propose models for these inferences based on the principle of rational action, and derive constrained functional trajectory optimization techniques for planning motion that is predictable or legible.Finally, we present experiments that test our work on novice users, and discuss the remaining challenges in enabling robots to generate such motion online in complex situations. 1

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Section: The Unpredictability Of Legibilitymentioning

confidence: 54%

Integrating human observer inferences into robot motion planning

Dragan

Srinivasa

2014

Auton Robot

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“…In reality, observers might get confused by the robot's behavior and stop reasoning about the robot's possible goals the way the model assumes they wouldcomparing the sub-optimality of its actions with respect to each of them. Instead, they might start believing that the robot is malfunctioning [30] or that it is not pursuing any of the goals -this is supported by our user studies in Sec. VII, which show that this belief significantly increases at higher C costs.…”

Section: B Parametersmentioning

confidence: 54%

“…This phenomenon stems from the difficulty in capturing how humans make inferences when faced with high levels of unpredictability [30]. We address this fundamental limitation by prohibiting the optimizer to "travel to uncharted territory", i.e.…”

Section: Introductionmentioning

confidence: 99%

Generating Legible Motion

Dragan¹,

Srinivasa²

2013

Robotics: Science and Systems IX

106

119

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Abstract-Legible motion -motion that communicates its intent to a human observer -is crucial for enabling seamless human-robot collaboration. In this paper, we propose a functional gradient optimization technique for autonomously generating legible motion. Our algorithm optimizes a legibility metric inspired by the psychology of action interpretation in humans, resulting in motion trajectories that purposefully deviate from what an observer would expect in order to better convey intent. A trust region constraint on the optimization ensures that the motion does not become too surprising or unpredictable to the observer.Our studies with novice users that evaluate the resulting trajectories support the applicability of our method and of such a trust region. They show that within the region, legibility as measured in practice does significantly increase. Outside of it, however, the trajectory becomes confusing and the users' confidence in knowing the robot's intent significantly decreases.

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“…Similarly, looking at short-term engagement, [10] found that a simple manipulation of the robot's behavior can lead to greater engagement. The authors let participants play several rounds of the rock-paper-scissors game with the robot (the playfulness of the scenario seems important).…”

Section: Towards Sustained Engagementmentioning

confidence: 99%

“…The authors found that empathetic robots are more likely to engage users in the long-term and they proposed several guidelines for designing such artificial companions. Other works [1,10] have shown that simpler ways to enhance engagement may as well be effective: [1] describe a series of longitudinal studies on engagement with an agent-like system. They demonstrated that user engagement with an interface agent can be increased using relatively simple techniques and manipulations that make the agent more life-like and human.…”

Section: Towards Sustained Engagementmentioning

confidence: 99%

You’re Doing It Wrong! Studying Unexpected Behaviors in Child-Robot Interaction

et al. 2015

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Abstract. We present a study on the impact of unexpected robot behaviors on the perception of a robot by children and their subsequent engagement in a playful interaction based on a novel "domino" task. We propose an original analysis methodology which blends behavioral cues and reported phenomenological perceptions into a compound index. While we found only a limited recognition of the different misbehaviors of the robot that we attribute to the age of the child participants (4-5 years old), interesting findings include a sustained engagement level, an unexpectedly low level of attribution of higher cognitive abilities and a negative correlation between anthropomorphic projections and actual behavioral engagement.

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No fair!! An interaction with a cheating robot

Cited by 159 publications

References 17 publications

Integrating human observer inferences into robot motion planning

Integrating human observer inferences into robot motion planning

Generating Legible Motion

You’re Doing It Wrong! Studying Unexpected Behaviors in Child-Robot Interaction

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