Rybkin, Oleh

24 publications

NeurIPS 2025 Compute-Optimal Scaling for Value-Based Deep RL Preston Fu, Oleh Rybkin, Zhiyuan Zhou, Michal Nauman, Pieter Abbeel, Sergey Levine, Aviral Kumar

ICML 2025 Latent Diffusion Planning for Imitation Learning Amber Xie, Oleh Rybkin, Dorsa Sadigh, Chelsea Finn

NeurIPS 2025 Real-World Reinforcement Learning of Active Perception Behaviors Edward S. Hu, Jie Wang, Xingfang Yuan, Fiona Luo, Muyao Li, Gaspard Lambrechts, Oleh Rybkin, Dinesh Jayaraman

ICML 2025 Value-Based Deep RL Scales Predictably Oleh Rybkin, Michal Nauman, Preston Fu, Charlie Victor Snell, Pieter Abbeel, Sergey Levine, Aviral Kumar

ICLRW 2025 Value-Based Deep RL Scales Predictably Oleh Rybkin, Michal Nauman, Preston Fu, Charlie Victor Snell, Pieter Abbeel, Sergey Levine, Aviral Kumar

ICLR 2024 METRA: Scalable Unsupervised RL with Metric-Aware Abstraction Seohong Park, Oleh Rybkin, Sergey Levine

ICLR 2024 Privileged Sensing Scaffolds Reinforcement Learning Edward S. Hu, James Springer, Oleh Rybkin, Dinesh Jayaraman

NeurIPSW 2023 METRA: Scalable Unsupervised RL with Metric-Aware Abstraction Seohong Park, Oleh Rybkin, Sergey Levine

NeurIPSW 2023 METRA: Scalable Unsupervised RL with Metric-Aware Abstraction Seohong Park, Oleh Rybkin, Sergey Levine

ICLR 2023 Planning Goals for Exploration Edward S. Hu, Richard Chang, Oleh Rybkin, Dinesh Jayaraman

ICLR 2022 Know Thyself: Transferable Visual Control Policies Through Robot-Awareness Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman

ICLRW 2022 Know Thyself: Transferable Visual Control Policies Through Robot-Awareness Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman

NeurIPS 2022 Learning General World Models in a Handful of Reward-Free Deployments Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip Ball, Oleh Rybkin, S Roberts, Tim Rocktäschel, Edward Grefenstette

NeurIPS 2021 Discovering and Achieving Goals via World Models Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak

ICMLW 2021 Discovering and Achieving Goals with World Models Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak

ICML 2021 Model-Based Reinforcement Learning via Latent-Space Collocation Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine

ICML 2021 Simple and Effective VAE Training with Calibrated Decoders Oleh Rybkin, Kostas Daniilidis, Sergey Levine

L4DC 2020 Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning Karl Pertsch, Oleh Rybkin, Jingyun Yang, Shenghao Zhou, Konstantinos Derpanis, Kostas Daniilidis, Joseph Lim, Andrew Jaegle

ECCV 2020 Learning Predictive Models from Observation and Interaction Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, Chelsea Finn

NeurIPS 2020 Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, Sergey Levine

ICML 2020 Planning to Explore via Self-Supervised World Models Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak

CoRL 2020 Reinforcement Learning with Videos: Combining Offline Observations with Interaction Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, Chelsea Finn

ECCVW 2020 Toward Continuous-Time Representations of Human Motion Weiyu Du, Oleh Rybkin, Lingzhi Zhang, Jianbo Shi

ICLR 2019 Learning What You Can Do Before Doing Anything Oleh Rybkin, Karl Pertsch, Konstantinos G. Derpanis, Kostas Daniilidis, Andrew Jaegle