Legible robot pointing

Holladay, Rachel; Dragan, Anca D.; Srinivasa, Siddhartha S.

doi:10.1109/roman.2014.6926256

Cited by 48 publications

(34 citation statements)

References 20 publications

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“…by replacing the notion of goal with that of an outcome -here, legible motion becomes motion that enables quick and confident inference of the desired outcome. Our recent work illustrated this for pointing gestures [20].…”

Section: Formalizing Legibility and Predictabilitymentioning

confidence: 83%

“…We are excited to investigate this formalism with other channels of communication and other robot morphologies. Recently, we showed the formalism's applicability to pointing gestures [20], Tellex et al [50] used the same underlying mathematics to generate legible natural language requests, and Szafir et al…”

Section: Discussionmentioning

confidence: 99%

“…shown to apply to non-human agents, including robots [20]. The robot could model this notion of "efficiency" via a cost function defining what it means to be efficient.…”

Section: B Connection To Action Interpretation In Psychologymentioning

confidence: 99%

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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: Formalizing Legibility and Predictabilitymentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

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Integrating human observer inferences into robot motion planning

Dragan

Srinivasa

2014

Auton Robot

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“…In summary, some past work has proposed heuristics for avoiding failures in robot interactions (e.g., [41,43,44]), and other work has been done on dynamically generating motions of a robot arm to be legible or deceptive [38,53], but none of these studies had investigated how to generate playful reaching motions. The contribution of the current work is exploring how people perceive playful reaching motions of various shapes and lengths intended to also avoid failures based on appearing helpful, clear, careful, and combining structure and anomaly.…”

Section: Playful Reachingmentioning

confidence: 99%

Avoiding Playfulness Gone Wrong: Exploring Multi-objective Reaching Motion Generation in a Social Robot

Cooney

Sant'Anna

2017

Int J of Soc Robotics

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Companion robots will be able to perform useful tasks in homes and public places, while also providing entertainment through playful interactions. "Playful" here means fun, happy, and humorous. A challenge is that generating playful motions requires a non-trivial understanding of how people attribute meaning and intentions. The literature suggests that playfulness can lead to some undesired impressions such as that a robot is obnoxious, untrustworthy, unsafe, moving in a meaningless fashion, or boring. To generate playfulness while avoiding such typical failures, we proposed a model for the scenario of a robot arm reaching for an object: some simplified movement patterns such as sinusoids are structured toward appearing helpful, clear about goals, safe, and combining a degree of structure and anomaly. We integrated our model into a mathematical framework (CHOMP) and built a new robot, Kakapo, to perform dynamically generated motions. The results of an exploratory user experiment were positive, suggesting that: Our proposed system was perceived as playful over the course of several minutes. Also a better impression resulted compared with an alternative playful system which did not use our proposed heuristics; furthermore a negative effect was observed for several minutes after showing the alternative motions, suggesting that failures are important to avoid. And, an inverted u-shaped correlation was observed between motion length and degree of perceived playfulness, suggesting that motions should neither be too short or too long and that length is also a factor which can be considered when generating playful motions. A short follow-up study provided some additional support for the idea that playful motions which seek to avoid failures can B Martin Cooney martin.daniel.cooney@gmail.com 1 School of Information Technology, Halmstad University, Halmstad, Halland, Sweden be perceived positively. Our intent is that these exploratory results will provide some insight for designing various playful robot motions, toward achieving some good interactions.

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“…The legibility and predictability of robot motion to the human is investigated [11], and different methods for generating legible robot motion are proposed [15]. We apply this concept of legibility however to the human control commands, such that the intent expressed in the human command is clear to the robot.…”

Section: Related Workmentioning

confidence: 99%

Mode Switch Assistance To Maximize Human Intent Disambiguation

Gopinath

Argall²

2017

Robotics: Science and Systems XIII

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Abstract-In this paper, we develop an algorithm for intent inference via goal disambiguation with a shared-control assistive robotic arm. Assistive systems are often required to infer human intent and this often is a bottleneck for providing assistance quickly and accurately. We introduce the notion of inverse legibility in which the human-generated actions are legible enough for the robot to infer the human intent confidently and accurately. The proposed disambiguation paradigm seeks to elicit legible control commands from the human by selecting control modes, for the robotic arm, in which human-directed motion will maximally disambiguate between multiple goals. We present simulation results which look into the robustness of our algorithm and the impact of the choice of confidence functions on the performance of the system. Our simulations results suggest that the choice of confidence function is a critical factor in determining the disambiguation algorithm's capability to capture human intent. We also present a pilot study that explores the efficacy of the algorithm on real hardware with promising preliminary results.

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Legible robot pointing

Cited by 48 publications

References 20 publications

Integrating human observer inferences into robot motion planning

Integrating human observer inferences into robot motion planning

Avoiding Playfulness Gone Wrong: Exploring Multi-objective Reaching Motion Generation in a Social Robot

Mode Switch Assistance To Maximize Human Intent Disambiguation

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