100 citations · 159 across the 14 of their papers we have counts for
6 papers · 1 filter
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
Ingmar Schubert, Danny Driess, Ozgur S. Oguz +1
Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand. On the other hand, approximate models are readily available in many robotics scenario…
RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning
Cornelius V. Braun, Joaquim Ortiz-Haro, Marc Toussaint +1
Sequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many obje…
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning
Zhehua Zhou, Ozgur S. Oguz, Yi Ren +2
Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process. For implementing saf…
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks
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. This issue has been addressed using potential-based reward…
Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly
Valentin Noah Hartmann, Andreas Orthey, Danny Driess +2
Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the rob…
Visualization of Nonlinear Programming for Robot Motion Planning
David Hägele, Moataz Abdelaal, Ozgur S. Oguz +2
Nonlinear programming targets nonlinear optimization with constraints, which is a generic yet complex methodology involving humans for problem modeling and algorithms for problem s…