no code implementations • 5 Dec 2023 • Erdi Sayar, Zhenshan Bing, Carlo D'Eramo, Ozgur S. Oguz, Alois Knoll
Multi-goal robot manipulation tasks with sparse rewards are difficult for reinforcement learning (RL) algorithms due to the inefficiency in collecting successful experiences.
no code implementations • 23 Nov 2023 • Arda Sarp Yenicesu, Berk Cicek, Ozgur S. Oguz
This study addresses the challenge of manipulation, a prominent issue in robotics.
1 code implementation • NeurIPS 2021 • Ingmar Schubert, Danny Driess, Ozgur S. Oguz, Marc Toussaint
Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand.
no code implementations • 10 Sep 2021 • Zhehua Zhou, Ozgur S. Oguz, Yi Ren, Marion Leibold, Martin Buss
Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process.
no code implementations • 14 Jul 2021 • Ingmar Schubert, Ozgur S. Oguz, Marc Toussaint
In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration.
no code implementations • 28 Jan 2021 • David Hägele, Moataz Abdelaal, Ozgur S. Oguz, Marc Toussaint, Daniel Weiskopf
Nonlinear programming targets nonlinear optimization with constraints, which is a generic yet complex methodology involving humans for problem modeling and algorithms for problem solving.
Motion Planning Robotics Human-Computer Interaction Numerical Analysis Numerical Analysis H.5.2; G.1.6