Joint action understanding improves robot-to-human object handover

Grigore, Elena Corina; Eder, Kerstin; Pipe, Anthony G.; Melhuish, Chris; Leonards, Ute

doi:10.1109/iros.2013.6697021

Cited by 75 publications

(37 citation statements)

References 21 publications

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“…Grigore et al [17] incorporated the human receiver's intention into the robot's release decision, by evaluating the receiver's focus of attention and engagement in the interaction based on human's eye gaze and head orientation. Nagata et al [37] proposed a force-torque sensing method for a robot to determine when to open its hand during object delivery.…”

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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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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“…However, taking a human partner's eye gaze into account when planning a handover increases the success of robot-to-human handovers [14]. More generally, human gaze is task driven, and gaze fixations are rarely directed to locations in the world that are not relevant to the task, even if they are visually salient [15] …”

Section: Related Workmentioning

confidence: 99%

Deliberate delays during robot-to-human handovers improve compliance with gaze communication

Admoni

Dragan

Srinivasa

et al. 2014

Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction

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As assistive robots become popular in factories and homes, there is greater need for natural, multi-channel communication during collaborative manipulation tasks. Non-verbal communication such as eye gaze can provide information without overloading more taxing channels like speech. However, certain collaborative tasks may draw attention away from these subtle communication modalities. For instance, robot-to-human handovers are primarily manual tasks, and human attention is therefore drawn to robot hands rather than to robot faces during handovers. In this paper, we show that a simple manipulation of a robot's handover behavior can significantly increase both awareness of the robot's eye gaze and compliance with that gaze. When eye gaze communication occurs during the robot's release of an object, delaying object release until the gaze is finished draws attention back to the robot's head, which increases conscious perception of the robot's communication. Furthermore, the handover delay increases peoples' compliance with the robot's communication over a non-delayed handover, even when compliance results in counterintuitive behavior.

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“…We believe this focus is critical as feeding is both a crucial component of our everyday lives and uniquely different in how we interact with the world. In that regard, our work echoes the application-specific work in human-robot interaction on handovers, also a crucial and unique act [24], [25], where the analysis and taxonomy of human-human handovers laid the foundation for algorithms for seamless human-robot handovers [24]- [26].…”

Section: Related Workmentioning

confidence: 59%

Towards Robotic Feeding: Role of Haptics in Fork-Based Food Manipulation

Bhattacharjee

Lee

Song

et al. 2019

IEEE Robot. Autom. Lett.

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Autonomous feeding is challenging because it requires manipulation of food items with various compliance, sizes, and shapes. To understand how humans manipulate food items during feeding and to explore ways to adapt their strategies to robots, we collected a rich dataset of human trajectories by asking them to pick up food and feed it to a mannequin. From the analysis of the collected haptic and motion signals, we demonstrate that humans adapt their control policies to accommodate to the compliance and shape of the food item being acquired. We propose a taxonomy of manipulation strategies for feeding to highlight such policies. As a first step to generate compliance-dependent policies, we propose a set of classifiers for compliance-based food categorization from haptic and motion signals. We compare these human manipulation strategies with fixed position-control policies via a robot. Our analysis of success and failure cases of human and robot policies further highlights the importance of adapting the policy to the compliance of a food item.

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Joint action understanding improves robot-to-human object handover

Cited by 75 publications

References 21 publications

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

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

Deliberate delays during robot-to-human handovers improve compliance with gaze communication

Towards Robotic Feeding: Role of Haptics in Fork-Based Food Manipulation

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