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Tactile-based learning and classification methods for task planning and verification ? applications to multi-digital and bimanual robotic manipulation

Technological challenge: Artificial intelligence & Data intelligence (learn more)

Department: Département Systèmes (LETI)

Laboratory: Laboratoire Signaux et Systèmes de Capteurs

Start Date: 01-09-2022

Location: Grenoble

CEA Code: SL-DRT-22-0295

Contact: saifeddine.aloui@cea.fr

The robotic manipulation of objects first of all requires a grasp planning of these objects, which is a function of the characteristic parameters of the considered hardware tools and the task to be performed (such as the accessibility areas or the level and direction of the efforts that may be involved in the tasks of assembly, insertion, dexterous manipulation, etc.). In addition, during the execution of the task, it is necessary to be able to ensure the nominal progress of the planned task, by detecting the occurrence of certain critical events necessary for its completion (such as the interaction of objects with each other, the loss of stability of the object, etc.) and then validating the actual completion of the planned task (via the classification of data that characterizes the success or failure of tasks such as insertion or assembly). These detection and verification steps, which are crucial when it comes to robotizing certain critical tasks requiring a high level of traceability, can be based in particular on the analysis and monitoring of data or signals specific to the handling system in question. The work requested will exploit an experimental system consisting of a two-handed station, equipped in particular with two multi-digital grippers equipped with multimodal tactile sensors developed by the CEA. This thesis work is essentially divided into two parts. The first part consists in the use of learning methods, which are able to take into account the capacities of the pluridigital manipulators and the imperatives of the task, to plan the grasping of the objects. The second part of the thesis aims at exploiting some methods based on the classification of tactile and proprioceptive signals of the system to validate the accomplishment of the task.

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