100 citations · 159 across the 14 of their papers we have counts for
15 papers
CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation
Berk Çiçek, Mert K. Er, Ozgur S. Oguz
While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understanding, applying them directly…
Push, Press, Slide: Mode-Aware Planar Contact Manipulation via Reduced-Order Models
Melih Özcan, Umut Orguner, Ozgur S. Oguz
Non-prehensile planar manipulation, including pushing and press-and-slide, is critical for diverse robotic tasks, but notoriously challenging due to hybrid contact mechanics, under…
MO-SeGMan: Rearrangement Planning Framework for Multi Objective Sequential and Guided Manipulation in Constrained Environments
Cankut Bora Tuncer, Marc Toussaint, Ozgur S. Oguz
In this work, we introduce MO-SeGMan, a Multi-Objective Sequential and Guided Manipulation planner for highly constrained rearrangement problems. MO-SeGMan generates object placeme…
An Optimization-Augmented Control Framework for Single and Coordinated Multi-Arm Robotic Manipulation
Melih Özcan, Ozgur S. Oguz
Robotic manipulation demands precise control over both contact forces and motion trajectories. While force control is essential for achieving compliant interaction and high-frequen…
Neural Field Representations of Articulated Objects for Robotic Manipulation Planning
Phillip Grote, Joaquim Ortiz-Haro, Marc Toussaint +1
Traditional approaches for manipulation planning rely on an explicit geometric model of the environment to formulate a given task as an optimization problem. However, inferring an…
Spatial Reasoning via Deep Vision Models for Robotic Sequential Manipulation
Hongyou Zhou, Ingmar Schubert, Marc Toussaint +1
In this paper, we propose using deep neural architectures (i.e., vision transformers and ResNet) as heuristics for sequential decision-making in robotic manipulation problems. This…
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…
Learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation
Joaquim Ortiz-Haro, Valentin N. Hartmann, Ozgur S. Oguz +1
Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a p…
Learning a Low-dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems
Zhehua Zhou, Ozgur S. Oguz, Marion Leibold +1
For safely applying reinforcement learning algorithms on high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning…
Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning
Valentin N. Hartmann, Ozgur S. Oguz, Danny Driess +2
Integrating robotic systems in architectural and construction processes is of core interest to increase the efficiency of the building industry. Automated planning for such systems…