SocRob@Home

Lima, Pedro U.; Azevedo, Carlos; Brzozowska, Emilia; Cartucho, João; Dias, Tiago; Gonçalves, João; Kinarullathil, Mithun; Lawless, Guilherme; Lima, Oscar; Luz, Rute; Miraldo, Pedro; Piazza, Enrico; Silva, Miguel; Veiga, Tiago S.; Ventura, Rodrigo

doi:10.1007/s13218-019-00618-w

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…However, many studies based on these competitions on household service robot systems are rather biased towards the system integration for certain benchmarks than discussing the generalization and have explored separately from the robot learning studies, while some recent solutions of these competitions partly leverage learning modules (e.g. off-theshelf object recognition modules) [12][13][14]. In the WRC2020 competition, we showcased a service robot system which extensively utilized data-driven modules for generalization and adaptation in household environments durable for real-world deployment.…”

Section: Introductionmentioning

confidence: 99%

World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator

Matsushima¹,

Noguchi²,

Arima³

et al. 2022

Preprint

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Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans. The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances. For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions. In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments.

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Section: Introductionmentioning

confidence: 99%