Feature-Augmented Visuomotor Learning for Accurate Identification of Manipulating Objects


Feature-Augmented Visuomotor Learning for Accurate Identification of Manipulating Objects – This paper describes a simple, yet effective technique to detect object-specific behaviors from deep networks of object-sensitive photometric sensors. An attention mechanism is designed to guide object detection by leveraging photometric information provided by object features. The attention mechanism is implemented by using a deep convolutional neural network (CNN) to map photometric patterns from the input to the target object features. The learned network is then used to learn a visual interpretation of the photometric features. We show that the proposed method outperforms the state-of-the-art tracking approaches. On the other hand, our proposed method is capable of achieving higher accuracy when compared to state-of-the-art object detection approaches.

Most state-of-the-art models for sequence labeling have been trained in reinforcement learning, but the learning process is more difficult to train. In this work, we propose a novel reinforcement learning-based reinforcement learning scenario where a reinforcement learning game system (RML) is trained on a dataset of objects. The resulting reinforcement learning scenario requires the agent to learn to place objects into the desired areas, and to retrieve objects from these areas to obtain the desired objects. In this scenario, both the agent and the RL system learn to place objects into two different locations, in the space of two different states and distances respectively, including the target and the desired objects. We show experimental results on the Atari 2600 dataset of objects, showing that we can effectively learn the state for objects and the space for objects, respectively.

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Feature-Augmented Visuomotor Learning for Accurate Identification of Manipulating Objects

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  • Fast Convergence of Bayesian Networks via Bayesian Network Kernels

    Unsupervised Learning with the Hierarchical Recurrent Neural NetworkMost state-of-the-art models for sequence labeling have been trained in reinforcement learning, but the learning process is more difficult to train. In this work, we propose a novel reinforcement learning-based reinforcement learning scenario where a reinforcement learning game system (RML) is trained on a dataset of objects. The resulting reinforcement learning scenario requires the agent to learn to place objects into the desired areas, and to retrieve objects from these areas to obtain the desired objects. In this scenario, both the agent and the RL system learn to place objects into two different locations, in the space of two different states and distances respectively, including the target and the desired objects. We show experimental results on the Atari 2600 dataset of objects, showing that we can effectively learn the state for objects and the space for objects, respectively.


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