2015 IEEE International Conference on Robotics and Automation (ICRA) 2015
DOI: 10.1109/icra.2015.7139823
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Unsupervised learning of multi-hypothesized pick-and-place task templates via crowdsourcing

Abstract: In order for robots to be useful in real world learning scenarios, non-expert human teachers must be able to interact with and teach robots in an intuitive manner. One essential robot capability is wide-area (mobile or nonstationary) pick-and-place tasks. Even in its simplest form, pick-and-place is a hard problem due to uncertainty arising from noisy input demonstrations and non-deterministic real world environments. This work introduces a novel method for goal-based learning from demonstration where we learn… Show more

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Cited by 13 publications
(17 citation statements)
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“…However, in contrast to these works, our approach learns latent organizational patterns across different users in a collaborative manner and without the need for designing features that describe objects or users. Recently, Toris et al (2015) presented an approach to learn placing locations of objects based on crowdsourcing data from many users. Their approach allows for learning multiple hypotheses for placing the same object, and for reasoning about the most likely frame of reference when learning the target poses.…”
Section: Related Workmentioning
confidence: 99%
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“…However, in contrast to these works, our approach learns latent organizational patterns across different users in a collaborative manner and without the need for designing features that describe objects or users. Recently, Toris et al (2015) presented an approach to learn placing locations of objects based on crowdsourcing data from many users. Their approach allows for learning multiple hypotheses for placing the same object, and for reasoning about the most likely frame of reference when learning the target poses.…”
Section: Related Workmentioning
confidence: 99%
“…Our approach is also able to capture multiple modes with respect to the preferred location for placing a certain object. In contrast to Toris et al (2015), we explicitly target learning patterns in user preferences with respect to sorting objects in different containers.…”
Section: Related Workmentioning
confidence: 99%
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“…For instance, [10] shows how robots can recover from difficult states or failures by asking for help. In [25,35,36], a robot learns from human demonstration and correction, while a robot performs object detection and recognition with human inputs in [23,28,32,39]. In computer vision, a number of work have focused on designing human-machine interfaces that allow the vision algorithm to ask for human's help when it encounters difficulties [4,9,30,40].…”
Section: A Related Workmentioning
confidence: 99%
“…There, however, still exist many tasks that robots cannot autonomously perform to a satisfactory level. As such, robots can benefit tremendously from seeking human's help, as has been demonstrated in recent work [16,17,27,36]. In most existing work, it is assumed that human performance is perfect.…”
Section: Introductionmentioning
confidence: 99%