Objectives
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To fuse multiple sensing modalities (touch, sound, and first and third person vision)
to accurately and robustly estimate the physical properties of objects
in noisy and potentially ambiguous environments;
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To develop a framework for recognition and manipulation of objects via cooperation with humans
by mimicking human capability of learning and adapting across a set of different manipulators,
tasks, sensing configurations and environments;
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To define learning architectures for multimodal sensory data and for aggregated data from different
environments;
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To continually improve the adaptability and robustness of the learned models,
and to generalise capabilities across tasks and sites.
Funding
A CHIST-ERA project for the Call 2017 on the topic Object recognition and manipulation by robots:
Data sharing and experiment reproducibility (ORMR). The project was funded by UK EPSRC grant
EP/S031715/1, France ANR grant ANR-18-CHR3-0006, and Swiss NSF grant 20CH21_180444. The project
run between February 2019 and December 2022.
More information: https://www.chistera.eu/projects/corsmal
