Social context perception for mobile robots

Nigam, Aastha; Riek, Laurel D.

doi:10.1109/iros.2015.7353883

Cited by 29 publications

(22 citation statements)

References 19 publications

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“…Building on this foundation, our future work will include incorporating human gross motion directly to the synchronization measurement step, instead of using pre-labelled events. Moreover, we are also planning to incorporate a decision module for robots, which will use the perceived knowledge to select the best decision from a set of options, based on the context [19], [48].…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…However, incorporating local sensor data will be more challenging as it might be more noisy due to occlusion and local movements. However, we will build on our prior multimodal fusion and others' robot-centric perception work to overcome this challenge [19], [49].…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…However, the focus of most of these methods are to recognize the activity of a single human, rather than to understand the a team's dynamics and how it might affect behavior. This understanding is critical in humanrobot interaction scenarios, as the "one human, one robot" paradigm is rarely seen in ecological settings [18], [19]. To make informed decisions, robots need to understand this context [10].…”

Section: Introductionmentioning

confidence: 99%

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Movement Coordination in Human–Robot Teams: A Dynamical Systems Approach

2016

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Abstract-In order to be effective teammates, robots need to be able to understand high-level human behavior to recognize, anticipate, and adapt to human motion. We have designed a new approach to enable robots to perceive human group motion in real-time, anticipate future actions, and synthesize their own motion accordingly. We explore this within the context of joint action, where humans and robots move together synchronously. In this paper, we present an anticipation method which takes high-level group behavior into account. We validate the method within a human-robot interaction scenario, where an autonomous mobile robot observes a team of human dancers, and then successfully and contingently coordinates its movements to "join the dance". We compared the results of our anticipation method to move the robot with another method which did not rely on high-level group behavior, and found our method performed better both in terms of more closely synchronizing the robot's motion to the team, and also exhibiting more contingent and fluent motion. These findings suggest that the robot performs better when it has an understanding of high-level group behavior than when it does not. This work will help enable others in the robotics community to build more fluent and adaptable robots in the future.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Discussion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Movement Coordination in Human–Robot Teams: A Dynamical Systems Approach

2016

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“…The context of an interruption includes known information about the user, the task, the environment, and the type of interruption [67]. Following the definition in prior work [47], we consider interruption context to include visually observable cues from the environment that may inform the robot of a human's interruptibility. In particular, we use:…”

Section: Perceiving Interruptibilitymentioning

confidence: 99%

“…In our examination, we first contribute an ordinal scale of interruptibility that can be used to rate the interruptibility of a person and to influence decisions on whether or not to interrupt them (Section 3). Second, derived from factors used by humans to gauge interruptibility [53], we propose using features for person state (motivated by prior work in robotics on the closely related problem of estimating human engagement [42]) and features for interruption context (inspired by cues to interruptibility context used in prior work [47]) to classify interruptibility (Section 4). Last, we introduce the non-temporal and temporal models that we evaluated (Section 5) and the dataset of person observations that the models were evaluated upon (Section 6).…”

Section: Introductionmentioning

confidence: 99%

Robot Classification of Human Interruptibility and a Study of Its Effects

Banerjee

Silva

Chernova

2018

J. Hum.-Robot Interact.

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As robots become increasingly prevalent in human environments, there will inevitably be times when the robot needs to interrupt a human to initiate an interaction. Our work introduces the first interruptibility-aware mobile-robot system, which uses social and contextual cues online to accurately determine when to interrupt a person. We evaluate multiple non-temporal and temporal models on the interruptibility classification task, and show that a variant of Conditional Random Fields (CRFs), the Latent-Dynamic CRF, is the most robust, accurate, and appropriate model for use on our system. Additionally, we evaluate different classification features and show that the observed demeanor of a person can help in interruptibility classification; but in the presence of detection noise, robust detection of object labels as a visual cue to the interruption context can improve interruptibility estimates. Finally, we deploy our system in a large-scale user study to understand the effects of interruptibility-awareness on human-task performance, robot-task performance, and on human interpretation of the robot's social aptitude. Our results show that while participants are able to maintain task performance, even in the presence of interruptions, interruptibility-awareness improves the robot's task performance and improves participant social perceptions of the robot.

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Human-Robot Teaming: Approaches from Joint Action and Dynamical Systems

Iqbal¹,

Riek²

2018

Humanoid Robotics: A Reference

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Social context perception for mobile robots

Cited by 29 publications

References 19 publications

Movement Coordination in Human–Robot Teams: A Dynamical Systems Approach

Movement Coordination in Human–Robot Teams: A Dynamical Systems Approach

Robot Classification of Human Interruptibility and a Study of Its Effects

Human-Robot Teaming: Approaches from Joint Action and Dynamical Systems

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