A Learning Control Algorithm with Experiments on a Chopsticks Robot

Vassileva, D.; Boiadjiev, George

doi:10.3182/20090909-4-jp-2010.00034

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…One wily and dexterous human even used chopsticks to pick-pocket cellphones [14]. Researchers have adopted the general design of chopsticks for various applications, such as meal assistance [15], [16], surgery [17], micro-manipulation [18], repetitive pick-and-place [19], [20] and articulated-hand design [21]- [23]. A chopsticks-wielding robot has a potentially versatile use case to pick up objects of diverse shape, size, weight distribution, and deformation.…”

Section: Introductionmentioning

confidence: 99%

Telemanipulation with Chopsticks: Analyzing Human Factors in User Demonstrations

Kamat

Wang

et al. 2020

Preprint

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Chopsticks constitute a simple yet versatile tool that humans have used for thousands of years to perform a variety of challenging tasks ranging from food manipulation to surgery. Applying such a simple tool in a diverse repertoire of scenarios requires significant adaptability. Towards developing autonomous manipulators with comparable adaptability to humans, we study chopsticks-based manipulation to gain insights into human manipulation strategies. We conduct a within-subjects user study with 25 participants, evaluating three different data-collection methods: normal chopsticks, motioncaptured chopsticks, and a novel chopstick telemanipulation interface. We analyze factors governing human performance across a variety of challenging chopstick-based grasping tasks. Although participants rated teleoperation as the least comfortable and most difficult-to-use method, teleoperation enabled users to achieve the highest success rates on three out of five objects considered. Further, we notice that subjects quickly learned and adapted to the teleoperation interface. Finally, while motion-captured chopsticks could provide a better reflection of how humans use chopsticks, the teleoperation interface can produce quality on-hardware demonstrations from which the robot can directly learn.

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