Model Identification for Robotic Manipulation

Le 20 août dès 11h, lors du prochain séminaire du CeRVIM, le professeur Abdeslam Boularias du Robot Learning Lab de la Rutgers School of Arts and Sciences, présentera ses récents travaux portant sur des techniques basées sur la physique et faisant un usage efficient des données pour l’identification de modèles d’objets manipulés.

  • 20 août 2020

11h00 à 12h00


En téléprésence


Conférence du professeur Abdeslam Boularias, du Robot Learning Lab, associé au Department of Computer Science de la Rutgers School of Arts and Sciences. La conférence sera présentée en anglais.

Résumé de la conférence

A popular approach in robot learning is model-free reinforcement learning (RL), where a control policy is learned directly from sensory inputs by trial and error without explicitly modeling the effects of the robot’s actions on the controlled objects or system. While this approach has proved to be very effective in learning motor skills, it suffers from several drawbacks in the context of object manipulation due to the fact that types of objects and their arrangements vary significantly across different tasks. An alternative approach that may address these issues more efficiently is model-based RL. A model in RL generally refers to a transition function that maps a state and an action into a probability distribution over possible next states.

In this talk, professsor Boularias will present my recent works on data-efficient physics-driven techniques for identifying models of manipulated objects. To perform a task in a new environment with unknown objects, a robot first identifies from sequences of images the 3D mesh models of the objects, as well as their physical properties such as their mass distributions, moments of inertia and friction coefficients. The robot then reconstructs in a physics simulation the observed scene, and predicts the motions of the objects when manipulated. The predicted motions are then used to select a sequence of actions to apply on the real objects. Simulated virtual worlds that are learned from data also offer safe environments for exploration and for learning model-free policies.

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