Cost-Based Anticipatory Action Selection for Human–Robot Fluency

Hoffman, Guy; Breazeal, Cynthia

doi:10.1109/tro.2007.907483

Cited by 139 publications

(81 citation statements)

References 13 publications

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“…Because this reduces the time spent on walking to the workbench and selecting parts and tools, this system improves the work efficiency and reduces unnecessary labor. Hoffman et al [4,5] also proposed a cooperative robot system that selects the next task by itself based on the validity and risk of each action. Hayakawa et al [6] proposed an assembly system whereby the robot works with the human by determining the work state based on a self-organizing map.…”

Section: Introductionmentioning

confidence: 99%

B-PaDY: robot co-worker in a bumper assembly line

2016

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In an automobile assembly line, many processes require control and human intervention. A human's dexterity and ability to react to unpredictable changes in production volume and product specifications are necessary for these processes. Thus, it remains difficult to rely on robots or other automated systems. Conversely, some tasks, such as moving to a tool rack and delivering parts, do not require human skill. In the bumper assembly process of automobile assembly, a worker must move to pick up the bumper twice in one procedure. This task may strain the worker and increase work time for each bumper. To address this situation, we developed a new co-worker robot, named B-PaDY, to work in cooperation with human workers on the automobile bumper assembly line. This co-worker robot is not directly involved in the assembly processes, but performs tasks to support human workers. The robot delivers the bumper to the worker on the production line at the right time. We propose hardware concepts and designs to ensure worker's safety and allow for handling of all bumpers types. This robot uses a suspended rail system and handles the bumper, attaching to its reverse side. Additionally, we propose a method to determine the proper delivery timing based on a logistic regression model and confirm its effectiveness by performing experiments. We statistically model the worker's state upon completion of the assembly task and stochastically determine the proper timing. By recognizing the worker's behavior and supporting the worker with appropriate timing, our system effectively supplies the bumper to the worker and improves work efficiency.

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Section: Introductionmentioning

confidence: 99%

B-PaDY: robot co-worker in a bumper assembly line

2016

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“…In the context of a reach, this can inform a robot of what the person is reaching toward. Hoffman and Breazeal [11] noted that collaborative robotic systems that are based on observations of the current state of the shared task experience a stop-and-go style interaction. This is in part due to the need to respond to the current world state, rather than what is about to happen.…”

Section: Current and Future Workmentioning

confidence: 99%

“…Many prototype collaborative robot systems work by monitoring progress toward task completion, which can create a stop-and-go style interaction. Hoffman and Breazeal [11] propose a system in which a robot performs predictive task selection based on confidence based on estimates of the validity and risk of each task selection in order to improve interaction fluency. Moon et al [17] demonstrate that a the performance of a brief gaze motion can improve the speed and perceived quality of robot-to-human handovers.…”

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confidence: 99%

Developing Robot Assistants with Communicative Cues for Safe, Fluent HRI

Hart

Sheikholeslami

Gleeson

et al. 2018

Foundations of Trusted Autonomy

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“…The work of Hoffman et al [13] in human-robot collaboration for task assembly is relevant as well. The authors present a Markov process framework that can anticipate user actions.…”

Section: Related Workmentioning

confidence: 99%

Towards a synchronised Grammars framework for adaptive musical human-robot collaboration

Sarabia¹,

Lee

Demiris³

2015

2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Abstract-We present an adaptive musical collaboration framework for interaction between a human and a robot. The aim of our work is to develop a system that receives feedback from the user in real time and learns the music progression style of the user over time. To tackle this problem, we represent a song as a hierarchically structured sequence of music primitives. By exploiting the sequential constraints of these primitives inferred from the structural information combined with user feedback, we show that a robot can play music in accordance with the user's anticipated actions. We use Stochastic Context-Free Grammars augmented with the knowledge of the learnt user's preferences.We provide synthetic experiments as well as a pilot study with a Baxter robot and a tangible music table. The synthetic results show the synchronisation and adaptivity features of our framework and the pilot study suggest these are applicable to create an effective musical collaboration experience.

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Cost-Based Anticipatory Action Selection for Human–Robot Fluency

Cited by 139 publications

References 13 publications

B-PaDY: robot co-worker in a bumper assembly line

B-PaDY: robot co-worker in a bumper assembly line

Developing Robot Assistants with Communicative Cues for Safe, Fluent HRI

Towards a synchronised Grammars framework for adaptive musical human-robot collaboration

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