Transparent role assignment and task allocation in human robot collaboration

Roncone, Alessandro; Mangin, Olivier; Scassellati, Brian

doi:10.1109/icra.2017.7989122

Cited by 84 publications

(100 citation statements)

References 18 publications

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“…Approaches in the first class aim at defining cooperation models, i.e., data structures modelling the task to be jointly carried out, while keeping flexibility and human preferences into account [44,45,3,10,15,46,11].…”

Section: Planning Techniques In Human-robot Cooperationmentioning

confidence: 99%

“…A similar approach is adopted in [46], where HTMs are substituted by Hierarchical Agent-based Task Planners (HATPs) and POMDPs are replaced by Petri Network Plans (PNPs). However, differently from the approach in [15], the work by Sebastiani and colleagues support on-line changes during plan execution. Finally, the work by Darvish and colleagues represents cooperation models using AND/OR graphs, and allows for a switch among different cooperation sequences at runtime [11], therefore allowing humans to redefine the sequence of tasks among a predefined set of choices.…”

Section: Planning Techniques In Human-robot Cooperationmentioning

confidence: 99%

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On the manipulation of articulated objects in human–robot cooperation scenarios

Capitanelli

Maratea

Mastrogiovanni

et al. 2018

Robotics and Autonomous Systems

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Articulated and flexible objects constitute a challenge for robot manipulation tasks, but are present in different real-world settings, including home and industrial environments. Current approaches to the manipulation of articulated and flexible objects employ ad hoc strategies to sequence and perform actions on them depending on a number of physical or geometrical characteristics related to those objects, as well as on an a priori classification of target object configurations.In this paper, we propose an action planning and execution framework, which (i) considers abstract representations of articulated or flexible objects, (ii) integrates action planning to reason upon such configurations and to sequence an appropriate set of actions with the aim of obtaining a target configuration provided as a goal, and (iii) is able to cooperate with humans to collaboratively carry out the plan.On the one hand, we show that a trade-off exists between the way articulated or flexible objects are perceived and how the system represents them. Such a trade-off greatly impacts on the complexity of the planning process. On the other hand, we demonstrate the system's capabilities in allowing humans to interrupt robot action execution, and -in general -to contribute to the whole manipulation process.Results related to planning performance are discussed, and examples with a Baxter dual-arm manipulator performing actions collaboratively with humans are shown.

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Section: Planning Techniques In Human-robot Cooperationmentioning

confidence: 99%

Section: Planning Techniques In Human-robot Cooperationmentioning

confidence: 99%

On the manipulation of articulated objects in human–robot cooperation scenarios

Capitanelli

Maratea

Mastrogiovanni

et al. 2018

Robotics and Autonomous Systems

View full text Add to dashboard Cite

show abstract

“…This selection criteria resulted in a wide variety of final applications, with works that e.g. focused on evaluating the communication between peers [21], [22], [29], [33], [1], leveraging the collaborative domain to target physical human-robot interactions [20], [35], or proposing a teaching scenario where the robot learns a task model [30], [37], [44]. This broad landscape of applications is then summarized in Table I in terms of the type of actions implemented on the robot platform.…”

Section: Domainmentioning

confidence: 99%

“…Consequently, it is possible to devise an experiment in which there is a clear compartmentalization of the roles the two partners have. Similarly to [38], [40], [1], specific actions (e.g. bringing components) can be assigned to the robot, whereas the human can be entrusted to perform only the actions that require fine manipulation skills (e.g.…”

Section: Human-robot Interaction Designmentioning

confidence: 99%

“…Lastly, specific limitations of the robotic platform itself constrain the task domain. For example, as highlighted in Section II, of the few works that actively use a robot to physically engage in a collaborative task, a common choice is to rely on flat-pack furniture [1], [2], [3], [4]. Whilst this solution is advantageous for a number of reasons (i.e.…”

Section: Introductionmentioning

confidence: 99%

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The HRC Model Set for Human-Robot Collaboration Research

Zeylikman

Widder

Roncone

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

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In this paper, we present a model set for designing human-robot collaboration (HRC) experiments. It targets a common scenario in HRC, which is the collaborative assembly of furniture, and it consists of a combination of standard components and custom designs. With this work, we aim at reducing the amount of work required to set up and reproduce HRC experiments, and we provide a unified framework to facilitate the comparison and integration of contributions to the field. The model set is designed to be modular, extendable, and easy to distribute. Importantly, it covers the majority of relevant research in HRC, and it allows tuning of a number of experimental variables that are particularly valuable to the field. Additionally, we provide a set of software libraries for perception, control and interaction, with the goal of encouraging other researchers to proactively contribute to our work.

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Human-Robot Collaborative Navigation Search Using Social Reward Sources

Dalmasso

Garrell

Jiménez

et al. 2019

Advances in Intelligent Systems and Computing

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This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: humanrobot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication. The models are validated through quantitative evaluation against both agents' individual performance and qualitative surveying of participants' perception. After that, the three proposed communication levels are compared against each other using the previous metrics.

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Transparent role assignment and task allocation in human robot collaboration

Cited by 84 publications

References 18 publications

On the manipulation of articulated objects in human–robot cooperation scenarios

On the manipulation of articulated objects in human–robot cooperation scenarios

The HRC Model Set for Human-Robot Collaboration Research

Human-Robot Collaborative Navigation Search Using Social Reward Sources

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