Rita Laezza
Rita Laezza is a Postdoc in the Automation group. Rita has a background in Biomedical Engineering at Chalmers, having specialized in Signals and Control, and completed her thesis on Deep Learning for myoelectric pattern recognition at the Biomechatronics and Neurorehabilitation Lab. Her PhD research focused on Reinforcement Learning (RL) for robotic manipulation of deformable objects. Her goal was to deal with the manipulation of deformable objects by combining Deep Neural Networks and control policies based on RL principles. This project ran in collaboration with WASP.
Showing 11 publications
Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects
Offline Reinforcement Learning for Shape Control of Deformable Linear Objects from Limited Real Data
Planning and Control for Cable-routing with Dual-arm Robot
ReForm: A Robot Learning Sandbox for Deformable Linear Object Manipulation
Learning Shape Control of Elastoplastic Deformable Linear Objects
Presenting ReForm, a Robot Learning Sandbox for Deformable Linear Object Manipulation
Shape Control of Elastoplastic Deformable Linear Objects through Reinforcement Learning
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