Sharing effort in planning human-robot handover tasks

Mainprice, Jim; Gharbi, Mamoun; Siméon, Thierry; Alami, Rachid

doi:10.1109/roman.2012.6343844

Cited by 62 publications

(52 citation statements)

References 20 publications

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“…(Sisbot et al, 2005) proposed a navigation planner that creates safe, legible, and socially acceptable paths for approaching a person for a handover. As a progression from this work, the motion planner was extended to account for the amount of human motion required to handover, allowing the robot to choose the best handover location based on context (Mainprice, Gharbi, Simeon, & Alami, 2012). …”

Section: Handover Behaviors For Robotsmentioning

confidence: 99%

Towards Seamless Human-Robot Handovers

Strabala

Lee

Dragan

et al. 2013

JHRI

221

147

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A handover is a complex collaboration, where actors coordinate in time and space to transfer control of an object. This coordination comprises two processes: the physical process of moving to get close enough to transfer the object, and the cognitive process of exchanging information to guide the transfer. Despite this complexity, we humans are capable of performing handovers seamlessly in a wide variety of situations, even when unexpected. This suggests a common procedure that guides all handover interactions. Our goal is to codify that procedure. To that end, we first study how people hand objects to each other in order to understand their coordination process and the signals and cues that they use and observe with their partners. Based on these studies, we propose a coordination structure for human-robot handovers that considers the physical and social-cognitive aspects of the interaction separately. This handover structure describes how people approach, reach out their hands, and transfer objects while simultaneously coordinating the what, when, and where of handovers: to agree that the handover will happen (and with what object), to establish the timing of the handover, and to decide the configuration at which the handover will occur. We experimentally evaluate human-robot handover behaviors that exploit this structure, and offer design implications for seamless human-robot handover interactions.

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Section: Handover Behaviors For Robotsmentioning

confidence: 99%

Towards Seamless Human-Robot Handovers

Strabala

Lee

Dragan

et al. 2013

JHRI

221

147

View full text Add to dashboard Cite

show abstract

“…[6,26,31]) in human-robot interaction (HRI) have investigated how a robot should approach a human to initiate a handover. Basili et al [6] investigated how a human giver holds an object and approaches a human receiver in order to hand over the object, and indicated that their findings are transferable to a robot giver.…”

Section: Robot-to-human Handovermentioning

confidence: 99%

Impacts of Robot Head Gaze on Robot-to-Human Handovers

Zheng

Moon

Croft

et al. 2015

Int J of Soc Robotics

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In this paper, we investigate the use of a robot's gaze to improve the timing and subjective experience of faceto-face robot-to-human handovers. Based on observations of human gaze behaviors during face-to-face human-human handovers, we implement various gaze behaviors on a PR2 humanoid robot. We conducted two consecutive robot-tohuman handover studies. Results show that when the robot continually gazes at a projected handover position while handing over an object, the human receivers reach for the object significantly earlier than when the robot looks down, away from the handover location; further, when the robot continually gazes at the receiver's face instead of the handover position, the receivers reach for the object even earlier. When the robot-instead of continually gazing at a location-transitions its gaze from the handover position to the receivers' face, or vice versa, the receivers' reach time did not improve; however, the receivers perceive these gaze transitions to better communicate handover timing than continual gazes. Finally, the receivers perceive the robot to be more likeable and anthropomorphic when it looks at their B Minhua Zheng face than when it does not. Findings from our studies indicate that robot's use of gaze can help improve both fluency and subjective experience of the robot-to-human handover interactions.

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“…We consider variants of feature functions that have been introduced in previous work to account for human-robot interaction constraints [27], [28], [5]. We use two types of features inspired by the proxemics theory [29] and experiments in neuroscience [1].…”

Section: Feature Functionsmentioning

confidence: 99%

Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning

Mainprice

Hayne

Berenson

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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To enable safe and efficient human-robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative replanning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from two participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict a human's motion for a given task by iteratively replanning a trajectory for a 23 DoF human kinematic model using the STOMP algorithm with the learned cost function in the presence of a moving collaborator. Our results suggest that our method outperforms baseline methods and generalizes well for tasks similar to those that were demonstrated.

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Sharing effort in planning human-robot handover tasks

Cited by 62 publications

References 20 publications

Towards Seamless Human-Robot Handovers

Towards Seamless Human-Robot Handovers

Impacts of Robot Head Gaze on Robot-to-Human Handovers

Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning

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