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Abstract:
To acquire rich repertoires of skills, robots must be able to learn from their own autonomously collected data. We'll describe a video-prediction model that predicts what a robot will see next, and show how this model can be used to solve complex manipulations tasks in real-world settings. Our model was trained on 44,000 video sequences, where the manipulator autonomously pushes various objects. Using the model, the robot is capable of moving objects that were not seen during training to desired locations, handling multiple objects and pushing objects around obstructions. Unlike other methods in robotic learning, video-prediction does not require any human labels. Our experiments show that the method achieves a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robotic learning. This session is for attendees that possess a basic understanding of convolutional and recurrent neural networks.
To acquire rich repertoires of skills, robots must be able to learn from their own autonomously collected data. We'll describe a video-prediction model that predicts what a robot will see next, and show how this model can be used to solve complex manipulations tasks in real-world settings. Our model was trained on 44,000 video sequences, where the manipulator autonomously pushes various objects. Using the model, the robot is capable of moving objects that were not seen during training to desired locations, handling multiple objects and pushing objects around obstructions. Unlike other methods in robotic learning, video-prediction does not require any human labels. Our experiments show that the method achieves a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robotic learning. This session is for attendees that possess a basic understanding of convolutional and recurrent neural networks.  Back
 
Topics:
AI and DL Research, IoT, Robotics & Drones, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8629
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