Closed-Loop Robot Task Planning Based on Referring Expressions

Kuhner, Daniel; Aldinger, Johannes; Burget, Felix; Göbelbecker, Moritz; Burgard, Wolfram; Nebel, Bernhard

doi:10.1109/iros.2018.8593371

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…A number of works have tried to deal with the problem of adapting a robot's behavior to the order of execution of a task by a human and many techniques have been proposed to generate behaviors more or less dynamically. These are the techniques (in nonexhaustive list): Planning and Scheduling [4,14,15,17], AND-OR Graphs [8], Behavior-Tree [18,22], Hierarchical Task Networks (HTN) [2,6,13], Game Theory [5], Markov Decision Process (MDP), or a combination of them [21]. Among these techniques, task planning and more precisely hierarchical planning is widely used and very effective to generate symbolic plans in a similar way of the human decomposition of complex tasks into simpler actions [3].…”

Section: Related Workmentioning

confidence: 99%

Adapting Cobot Behavior to Human Task Ordering Variability for Assembly Tasks

HMEDAN

Kilgus

Fiorino

et al. 2022

FLAIRS

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In this paper, we address flexible assembly plans generation to accommodate human task variability. Unlike existing approaches, the proposed approach is based on a free-style human-robot interaction (HRI) that does not impose any task order on the participants and furthermore can accommodate their errors and help them to correct the errors without stopping the whole assembly process. Our approach is implemented in a real robotic architecture that combines sensory-motor modules with Hierarchical Task Networks (HTN) to endow the cobot with the necessary adaptability to correspond to human actions dynamically. We show experimentally with 56 participants on a simulated industrial assembly task that the cobot increases the task performance (reduction in the number of errors and gestures) without increasing the participants' cognitive load.

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Section: Related Workmentioning

confidence: 99%

Adapting Cobot Behavior to Human Task Ordering Variability for Assembly Tasks

HMEDAN

Kilgus

Fiorino

et al. 2022

FLAIRS

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show abstract

“…Previous work has addressed knowledge engineering tools for constructing planning domains but usually require PDDL experts (PDDL Studio [38]), or common knowledge in software engineering (GIPO [43], itSIMPLE [47]). There has been previous work on integrating task planning with robotic systems [12,23], learning high-level actions through natural language instructions [42] or learning preconditions and effects of actions to be used in planning [20,22,46]. However, in all of these cases, the robot is provided with a fixed set of low-level motor skills.…”

Section: Related Workmentioning

confidence: 99%

iRoPro: An interactive Robot Programming Framework

Liang

Pellier

Fiorino

et al. 2021

Int J of Soc Robotics

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Skill-based programming has been widely studied in robotic systems because it facilitates reusability [12], [13]. Programmers are often given flexibility to choose different skills and parameters based on their preferences [14], [15]. However, most of the extant works do not pay attention to the impact on the effectiveness of the skill monitoring modules when different skill parameters are admissible.…”

Section: Related Workmentioning

confidence: 99%

SQRP: Sensing Quality-aware Robot Programming System for Non-expert Programmers

Hsieh¹,

Huang²,

Mok³

2021

Preprint

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Robot programming typically makes use of a set of mechanical skills that is acquired by machine learning. Because there is in general no guarantee that machine learning produces robot programs that are free of surprising behavior, the safe execution of a robot program must utilize monitoring modules that take sensor data as inputs in real time to ensure the correctness of the skill execution. Owing to the fact that sensors and monitoring algorithms are usually subject to physical restrictions and that effective robot programming is sensitive to the selection of skill parameters, these considerations may lead to different sensor input qualities such as the view coverage of a vision system that determines whether a skill can be successfully deployed in performing a task. Choosing improper skill parameters may cause the monitoring modules to delay or miss the detection of important events such as a mechanical failure. These failures may reduce the throughput in robotic manufacturing and could even cause a destructive system crash. To address above issues, we propose a sensing quality-aware robot programming system that automatically computes the sensing qualities as a function of the robot's environment and uses the information to guide non-expert users to select proper skill parameters in the programming phase. We demonstrate our system framework on a 6DOF robot arm for an object pick-up task.

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Closed-Loop Robot Task Planning Based on Referring Expressions

Cited by 7 publications

References 22 publications

Adapting Cobot Behavior to Human Task Ordering Variability for Assembly Tasks

Adapting Cobot Behavior to Human Task Ordering Variability for Assembly Tasks

iRoPro: An interactive Robot Programming Framework

SQRP: Sensing Quality-aware Robot Programming System for Non-expert Programmers

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