Handing Over Objects to Human in a Friendly and Comfortable Manner

Xie, Biyun; Zhao, Jingjin

doi:10.1142/s0219843615500127

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…After removing duplicates ( n = 3,639 removed) and after title and abstract screening ( n = 5,435 removed), the full text of 18 studies were scanned and 10 studies were found to meet our eligibility criteria [ n = 8 removed: no active handover = 4 ( Salleh et al, 2011 ; Parastegari et al, 2018 ; Kato et al, 2019 ; Neranon, 2020 ), non-relevant outcome = 2 ( Korkiakangas et al, 2014 ; Carfì et al, 2019 ), no human kinematic or force data = 2 ( Xie and Zhao, 2015 ; Chan et al, 2020 )]. Thus, we included 10 studies in our systematic review that investigated the characteristics of human-human handover actions and their influencing factors, such as the handover object weight and availability of sensory information ( Mason and Mackenzie, 2005 ; Becchio et al, 2008 ; Gonzalez et al, 2011 ; Meyer et al, 2013 ; Hansen et al, 2017 ; Controzzi et al, 2018 ; Bekemeier et al, 2019 ; Cini et al, 2019 ; Döhring et al, 2020 ; Sutiphotinun et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

A systematic review of handover actions in human dyads

2023

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IntroductionHandover actions are joint actions in which an object is passed from one actor to another. In order to carry out a smooth handover action, precise coordination of both actors’ movements is of critical importance. This requires the synchronization of both the kinematics of the reaching movement and the grip forces of the two actors during the interaction. Psychologists, for example, may be interested in studying handover actions in order to identify the cognitive mechanisms underlying the interaction of two partners. In addition, robotic engineers may utilize insights from sensorimotor information processing in human handover as models for the design controllers in robots in hybrid (human-robot) interaction scenarios. To date, there is little knowledge transfer between researchers in different disciplines and no common framework or language for the study of handover actions.MethodsFor this reason, we systematically reviewed the literature on human-human handover actions in which at least one of the two types of behavioral data, kinematics or grip force, was measured.ResultsNine relevant studies were identified. The different methodologies and results of the individual studies are here described and contextualized.DiscussionBased on these results, a common framework is suggested that, provides a distinct and straightforward language and systematics for use in future studies. We suggest to term the actors as giver and receiver, as well as to subdivide the whole action into four phases: (1) Reach and grasp, (2) object transport, (3) object transfer, and (4) end of handover to comprehensively and clearly describe the handover action. The framework aims to foster the necessary exchange between different scientific disciplines to promote research on handover actions. Overall, the results support the assumption that givers adapt their executions according to the receiver’s intentions, that the start of the release of the object is processed feedforward and that the release process is feedback-controlled in the transfer phase. We identified the action planning of the receiver as a research gap.

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Section: Resultsmentioning

confidence: 99%

A systematic review of handover actions in human dyads

2023

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“…The First-order Retraction RRT (FR-RRT) algorithm 30 was used by Xie and Zhao 31 to generate collision-free trajectories for a four-DOF robotic manipulator. Here, the authors presented a method to plan handing-over movements during HRIs under the consideration of a given target position for the passage of an object.…”

Section: Related Workmentioning

confidence: 99%

A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots

Gulletta

Silva

Erlhagen

et al. 2021

International Journal of Advanced Robotic Systems

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As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions.

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Communication Through Haptics During Human Collaborative Manipulation

Javaid

2018

Int. J. Human. Robot.

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This paper describes research towards understanding haptic communication during planar object manipulation. In particular, a classification algorithm that classifies four stages of manipulation of a planar object is described. This research was performed as a part of a broader research project which has the goal of developing a user-friendly communication interface for an elderly-assistive robot. The manipulation of planar object was studied in detail as it happened very frequently during user study involving a caregiver helping an elderly person with the activities of daily living. For observing human haptic interaction, a sensory glove was developed. Further data collection was conducted in the laboratory setting and data was analyzed using various machine learning techniques. Based on this analysis, decision rules were derived that give insight into human-to-human collaborative manipulation of planar objects and successfully identified several classes of manipulative actions. The developed decision tree-based algorithm was then tested on the data of a user study that involved a caregiver assisting an elderly person in the activities of daily living. The developed algorithm also successfully classifies manipulation actions in real-time. This information is particularly interesting as it does not depend on any particular sensor and thus can be used by other researchers to further study haptic communication.

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Handing Over Objects to Human in a Friendly and Comfortable Manner

Cited by 4 publications

References 23 publications

A systematic review of handover actions in human dyads

A systematic review of handover actions in human dyads

A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots

Communication Through Haptics During Human Collaborative Manipulation

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