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GTI: Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

It takes a lot of data for robots to autonomously learn to perform simple manipulation tasks as as grasping and pushing. For example, prior work12 has leveraged Deep Reinforcement Learning to train robots to grasp and stack various objects. These tasks are usually short and relatively simple - for example, picking up a plastic bottle in a tray. However, because reinforcement learning relies on gaining experiences through trial-and-error, hundreds of robot hours were required for the robot to learn to picking up objects reliably. Quillen, D., Jang, E., Nachum, O., Finn, C., Ibarz, J., & Levine, S. (2018, May). Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6284-6291). IEEE. ↩ Cabi, S., Colmenarejo, S. G., Novikov, A., Konyushkova, K., Reed, S., Jeong, R., … & Sushkov, O. (2019). A Framework for Data-Driven Robotics. arXiv preprint arXiv:1909.12200. ↩



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GTI: Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

https://ai.stanford.edu/blog/gti

It takes a lot of data for robots to autonomously learn to perform simple manipulation tasks as as grasping and pushing. For example, prior work12 has leveraged Deep Reinforcement Learning to train robots to grasp and stack various objects. These tasks are usually short and relatively simple - for example, picking up a plastic bottle in a tray. However, because reinforcement learning relies on gaining experiences through trial-and-error, hundreds of robot hours were required for the robot to learn to picking up objects reliably. Quillen, D., Jang, E., Nachum, O., Finn, C., Ibarz, J., & Levine, S. (2018, May). Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6284-6291). IEEE. ↩ Cabi, S., Colmenarejo, S. G., Novikov, A., Konyushkova, K., Reed, S., Jeong, R., … & Sushkov, O. (2019). A Framework for Data-Driven Robotics. arXiv preprint arXiv:1909.12200. ↩



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https://ai.stanford.edu/blog/gti

GTI: Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

It takes a lot of data for robots to autonomously learn to perform simple manipulation tasks as as grasping and pushing. For example, prior work12 has leveraged Deep Reinforcement Learning to train robots to grasp and stack various objects. These tasks are usually short and relatively simple - for example, picking up a plastic bottle in a tray. However, because reinforcement learning relies on gaining experiences through trial-and-error, hundreds of robot hours were required for the robot to learn to picking up objects reliably. Quillen, D., Jang, E., Nachum, O., Finn, C., Ibarz, J., & Levine, S. (2018, May). Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6284-6291). IEEE. ↩ Cabi, S., Colmenarejo, S. G., Novikov, A., Konyushkova, K., Reed, S., Jeong, R., … & Sushkov, O. (2019). A Framework for Data-Driven Robotics. arXiv preprint arXiv:1909.12200. ↩

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      It takes a lot of data for robots to autonomously learn to perform simple manipulation tasks as as grasping and pushing. For example, prior work12 has leveraged Deep Reinforcement Learning to train robots to grasp and stack various objects. These tasks are usually short and relatively simple - for example, picking up a plastic bottle in a tray. However, because reinforcement learning relies on gaining experiences through trial-and-error, hundreds of robot hours were required for the robot to learn to picking up objects reliably. Quillen, D., Jang, E., Nachum, O., Finn, C., Ibarz, J., & Levine, S. (2018, May). Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6284-6291). IEEE. ↩ Cabi, S., Colmenarejo, S. G., Novikov, A., Konyushkova, K., Reed, S., Jeong, R., … & Sushkov, O. (2019). A Framework for Data-Driven Robotics. arXiv preprint arXiv:1909.12200. ↩
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      We developed Generalization Through Imitation (GTI) - an algorithm for learning visuomotor control from human demonstrations and generalizing to new long-horizon tasks by leveraging latent compositional structures.
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