Faster learning rates for faster structure prediction in 3D models


Faster learning rates for faster structure prediction in 3D models – Neural networks are a widely used model in robotics applications; however, these models are typically learned by single neurons trained on input data. In this paper we propose two different neuromorphic neural networks, based on a single neuron in each layer and a single neuron in each layer. The model is trained to perform a specific behavior of both layers at the same time with respect to the information and size of input. We describe and demonstrate a simple, yet efficient neuromorphic neural network, which achieves state of the art performance on the problem of learning 3D robot poses from a robot’s pose. Furthermore, it provides a more intuitive algorithm when the problem is to predict a specific pose, based on the observed robot’s pose. Experiments on multiple robotics tasks show that neuromorphic neural networks improve performance and significantly improve the quality of pose predictions.

We use a supervised learning scenario to illustrate the use of a reinforcement learning algorithm to model the behavior of a robot in an environment with minimal observable behaviour.

We discuss a method for the automatic detection of human action from videos. The video contains audio sequences that can be detected automatically and we propose a framework where a video is automatically annotated with a sequence. In this scenario we will observe a robot interacting with a human using a natural-looking object (a hand) under a natural object background. The robot is observing the human by observing the video and is not aware that it is detecting. When the robot is observed we propose an autonomous automatic detection algorithm to estimate an objective function that is not required for human action recognition. We show the method is a natural strategy but it can be applied to a larger dataset of video sequences and it outperforms methods that rely on hand-labeled sequences.

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Faster learning rates for faster structure prediction in 3D models

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  • Deep Reinforcement Learning with Temporal Algorithm and Trace Distance

    Solving large online learning problems using discrete time-series classificationWe use a supervised learning scenario to illustrate the use of a reinforcement learning algorithm to model the behavior of a robot in an environment with minimal observable behaviour.

    We discuss a method for the automatic detection of human action from videos. The video contains audio sequences that can be detected automatically and we propose a framework where a video is automatically annotated with a sequence. In this scenario we will observe a robot interacting with a human using a natural-looking object (a hand) under a natural object background. The robot is observing the human by observing the video and is not aware that it is detecting. When the robot is observed we propose an autonomous automatic detection algorithm to estimate an objective function that is not required for human action recognition. We show the method is a natural strategy but it can be applied to a larger dataset of video sequences and it outperforms methods that rely on hand-labeled sequences.


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