Determining Exception Context in Assembly Operations from Multimodal Data.

ABSTRACT: Robot assembly tasks can fail due to unpredictable errors and can only continue with the manual intervention of a human operator. Recently, we proposed an exception strategy learning framework based on statistical learning and context determination, which can successfully resolve such situations. This paper deals with context determination from multimodal data, which is the key component of our framework. We propose a novel approach to generate unified low-dimensional context descriptions based on image and force-torque data. For this purpose, we combine a state-of-the-art neural network model for image segmentation and contact point estimation using force-torque measurements. An ensemble of decision trees is used to combine features from the two modalities. To validate the proposed approa

SUBMITTER: Simonic M 

PROVIDER: S-EPMC9610822 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature