no code implementations • ICML 2020 • Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak
To solve complex tasks, intelligent agents first need to explore their environments.
1 code implementation • 13 Oct 2023 • Seohong Park, Oleh Rybkin, Sergey Levine
Through our experiments in five locomotion and manipulation environments, we demonstrate that METRA can discover a variety of useful behaviors even in complex, pixel-based environments, being the first unsupervised RL method that discovers diverse locomotion behaviors in pixel-based Quadruped and Humanoid.
1 code implementation • 23 Mar 2023 • Edward S. Hu, Richard Chang, Oleh Rybkin, Dinesh Jayaraman
We address this question within the goal-conditioned reinforcement learning paradigm, by identifying how the agent should set its goals at training time to maximize exploration.
no code implementations • 23 Oct 2022 • Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen J. Roberts, Tim Rocktäschel, Edward Grefenstette
We then present CASCADE, a novel approach for self-supervised exploration in this new setting.
2 code implementations • NeurIPS 2021 • Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak
How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision?
no code implementations • ICLR 2022 • Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman
Training visual control policies from scratch on a new robot typically requires generating large amounts of robot-specific data.
1 code implementation • 19 Jul 2021 • Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman
Training visual control policies from scratch on a new robot typically requires generating large amounts of robot-specific data.
1 code implementation • 24 Jun 2021 • Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine
The resulting latent collocation method (LatCo) optimizes trajectories of latent states, which improves over previously proposed shooting methods for visual model-based RL on tasks with sparse rewards and long-term goals.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • ICML Workshop URL 2021 • Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak
How can an artificial agent learn to solve a wide range of tasks in a complex visual environment in the absence of external supervision?
1 code implementation • 12 Nov 2020 • Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, Chelsea Finn
In this paper, we consider the question: can we perform reinforcement learning directly on experience collected by humans?
1 code implementation • NeurIPS 2020 • Karl Pertsch, Oleh Rybkin, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
In this work we propose a framework for visual prediction and planning that is able to overcome both of these limitations.
1 code implementation • 23 Jun 2020 • Oleh Rybkin, Kostas Daniilidis, Sergey Levine
We perform the first comprehensive comparative analysis of calibrated decoder and provide recommendations for simple and effective VAE training.
4 code implementations • 12 May 2020 • Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak
Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge.
no code implementations • ECCV 2020 • Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, Chelsea Finn
Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works, and then use this learned model to plan coordinated sequences of actions to bring about desired outcomes.
no code implementations • 25 Sep 2019 • Oleh Rybkin, Karl Pertsch, Frederik Ebert, Dinesh Jayaraman, Chelsea Finn, Sergey Levine
Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video.
no code implementations • 25 Sep 2019 • Karl Pertsch, Oleh Rybkin, Jingyun Yang, Konstantinos G. Derpanis, Kostas Daniilidis, Joseph J. Lim, Andrew Jaegle
To flexibly and efficiently reason about temporal sequences, abstract representations that compactly represent the important information in the sequence are needed.
no code implementations • L4DC 2020 • Karl Pertsch, Oleh Rybkin, Jingyun Yang, Shenghao Zhou, Konstantinos G. Derpanis, Kostas Daniilidis, Joseph Lim, Andrew Jaegle
We propose a model that learns to discover these important events and the times when they occur and uses them to represent the full sequence.
no code implementations • ICLR 2019 • Oleh Rybkin, Karl Pertsch, Konstantinos G. Derpanis, Kostas Daniilidis, Andrew Jaegle
We introduce a loss term that encourages the network to capture the composability of visual sequences and show that it leads to representations that disentangle the structure of actions.
no code implementations • 26 Mar 2018 • Andrew Jaegle, Oleh Rybkin, Konstantinos G. Derpanis, Kostas Daniilidis
We couple this latent state with a recurrent neural network (RNN) core that predicts future frames by transforming past states into future states by applying the accumulated state transformation with a learned operator.