Predicting the Future with Transformational States. - Andrew Jaegle; Oleh Rybkin; Konstantinos G. Derpanis; Kostas Daniilidis (CoRR, 2018)

Unsupervised Learning of Sensorimotor Affordances by Stochastic Future Prediction. - Oleh Rybkin; Karl Pertsch; Andrew Jaegle; Konstantinos G. Derpanis; Kostas Daniilidis (CoRR, 2018)

Learning what you can do before doing anything. - Oleh Rybkin; Karl Pertsch; Konstantinos G. Derpanis; Kostas Daniilidis; Andrew Jaegle (ICLR (Poster), 2019)

KeyIn: Discovering Subgoal Structure with Keyframe-based Video Prediction. - Karl Pertsch; Oleh Rybkin; Jingyun Yang; Konstantinos G. Derpanis; Joseph J. Lim; Kostas Daniilidis; Andrew Jaegle (CoRR, 2019)

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

Toward Continuous-Time Representations of Human Motion. - Weiyu Du; Oleh Rybkin; Lingzhi Zhang; Jianbo Shi (ECCV Workshops (6), 2020)

Learning Predictive Models from Observation and Interaction. - Karl Schmeckpeper; Annie Xie; Oleh Rybkin; Stephen Tian; Kostas Daniilidis; Sergey Levine; Chelsea Finn (ECCV (20), 2020)

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

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

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

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. - Karl Pertsch; Oleh Rybkin; Frederik Ebert; Chelsea Finn; Dinesh Jayaraman; Sergey Levine (CoRR, 2020)

Simple and Effective VAE Training with Calibrated Decoders. - Oleh Rybkin; Kostas Daniilidis; Sergey Levine (ICML, 2021)

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

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

Know Thyself: Transferable Visuomotor Control Through Robot-Awareness. - Edward S. Hu; Kun Huang; Oleh Rybkin; Dinesh Jayaraman (CoRR, 2021)

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

Learning General World Models in a Handful of Reward-Free Deployments. - Yingchen Xu; Jack Parker-Holder; Aldo Pacchiano; Philip J. Ball; Oleh Rybkin; Stephen Roberts 001; Tim Rocktäschel; Edward Grefenstette (NeurIPS, 2022)

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

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

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

Value-Based Deep RL Scales Predictably. - Oleh Rybkin; Michal Nauman; Preston Fu; Charlie Snell; Pieter Abbeel; Sergey Levine; Aviral Kumar (CoRR, 2025)

Video2Policy: Scaling up Manipulation Tasks in Simulation through Internet Videos. - Weirui Ye; Fangchen Liu; Zheng Ding; Yang Gao 029; Oleh Rybkin; Pieter Abbeel (CoRR, 2025)

Latent Diffusion Planning for Imitation Learning. - Amber Xie; Oleh Rybkin; Dorsa Sadigh; Chelsea Finn (CoRR, 2025)