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Learning for Intelligent Robotic Agents

Welcome to LiRA, where we focus on developing embodied AI agents. Unlike traditional passive AI systems, we specialize in crafting active AI agents capable of understanding and dynamically interacting with the physical world, much like humans do. Our goal extends to the seamless coexistence and collaboration between humans and robots, envisioning a future where these entities work harmoniously together. Drawing from an interdisciplinary toolkit, our research leverages methodologies from reinforcement learning, machine learning, deep learning, optimization, and probabilistic inference.

news

Jul 21, 2026 LiRA Lab participated in the 7th Turkish Robotics Conference held at Koç University, Istanbul, on July 19–21, 2026. Ece Kunduracioglu and Huzaifa Salahuddin delivered workshop presentations on behalf of Asst. Prof. Özgür S. Öğüz on the first day, while Asst. Prof. Öğüz gave a talk on the final day. Several lab members also presented their research during the poster sessions.
Jul 17, 2026 Our paper, “CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation”, by Berk Çiçek, Mert Kaan Er, and Özgür S. Öğüz, was published in the proceedings of Robotics: Science and Systems (RSS 2026).
Jul 11, 2026 Our paper, “Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies”, by Hikmet Şimşir and Özgür S. Öğüz, was published in the proceedings of the 43rd International Conference on Machine Learning (ICML 2026).
Jun 30, 2026 Our paper, “Uncertainty-Aware Safety Propagation Critics for Safe Reinforcement Learning”, by Kutay Demiray, Efe Eren Ceyani, and Özgür S. Öğüz, was published in Transactions on Machine Learning Research (TMLR) in June 2026.

selected publications

  1. USPC_representative.png
    Uncertainty-Aware Safety Propagation Critics for Safe Reinforcement Learning
    Kutay Demiray , Efe Eren Ceyani , and Ozgur S Oguz
    Transactions on Machine Learning Research, 2026
  2. CoRAL_representative.png
    CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation
    Berk Cicek , Mert K Er , and Ozgur S Oguz
    RSS 2026 Robotics: Science and Systems 2026, 2026
  3. lagrangian_representative.png
    Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies
    Hikmet Simsir , and Ozgur S Oguz
    ICML 2026 Forty-Third International Conference on Machine Learning, 2026
  4. Mo-Segman_representative.png
    MO-SeGMan: Rearrangement Planning Framework for Multi Objective Sequential and Guided Manipulation in Constrained Environments
    Cankut Bora Tuncer , Marc Toussaint , and Ozgur S Oguz
    arXiv preprint arXiv:2511.01476, 2025
  5. SeGMan_representative.png
    SeGMan: Sequential and Guided Manipulation Planner for Robust Planning in 2D Constrained Environments
    Cankut Bora Tuncer , Dilruba Sultan Haliloglu , and Ozgur S Oguz
    2025
  6. waytu-framework.png
    Way-Tu: A Framework for Tool Selection and Manipulation Using Waypoint Representations
    Ece Kunduracioglu , and Ozgur S Oguz
    In Dexterous Manipulation: Learning and Control with Diverse Modalities , 2025
  7. H-Map_representative.png
    H-MaP: An Iterative and Hybrid Sequential Manipulation Planner
    Berk Cicek , Arda Sarp Yenicesu , Cankut Bora Tuncer , and 2 more authors
    IEEE Robotics and Automation Letters, 2025
  8. CUER_representative_image.png
    CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms
    Arda Sarp Yenicesu , Furkan B. Mutlu , Suleyman S. Kozat , and 1 more author
    2024

Bilkent University
Department of Computer Engineering
Cankaya 06800 Ankara