Active perception for object manipulation

Mezei, Ady-Daniel; Tamás, Levente

doi:10.1109/iccp.2016.7737158

Cited by 3 publications

(1 citation statement)

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“…In turn, detection includes acquisition, preprocessing and classification steps. Several components of the pipeline are similar to the ones presented in [24], what is different is the classification algorithm and module used, and most importantly, the presence of a planning module in the pipeline.…”

Section: Active Perception Pipelinementioning

confidence: 99%

Sorting Objects from a Conveyor Belt Using POMDPs with Multiple-Object Observations and Information-Gain Rewards

Mezei

Tamás

Buşoniu

2020

Sensors

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We consider a robot that must sort objects transported by a conveyor belt into different classes. Multiple observations must be performed before taking a decision on the class of each object, because the imperfect sensing sometimes detects the incorrect object class. The objective is to sort the sequence of objects in a minimal number of observation and decision steps. We describe this task in the framework of partially observable Markov decision processes, and we propose a reward function that explicitly takes into account the information gain of the viewpoint selection actions applied. The DESPOT algorithm is applied to solve the problem, automatically obtaining a sequence of observation viewpoints and class decision actions. Observations are made either only for the object on the first position of the conveyor belt or for multiple adjacent positions at once. The performance of the single- and multiple-position variants is compared, and the impact of including the information gain is analyzed. Real-life experiments with a Baxter robot and an industrial conveyor belt are provided.

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Section: Active Perception Pipelinementioning

confidence: 99%