• Corpus ID: 28144509

Neural Network Memory Architectures for Autonomous Robot Navigation

@article{Chen2017NeuralNM,
  title={Neural Network Memory Architectures for Autonomous Robot Navigation},
  author={Steven W. Chen and Nikolay A. Atanasov and Arbaaz Khan and Konstantinos Karydis and Daniel D. Lee and Vijay R. Kumar},
  journal={ArXiv},
  year={2017},
  volume={abs/1705.08049}
}
Author(s): Chen, Steven; Atanasov, Nikolay; Khan, Arbaaz; Karydis, Konstantinos; Lee, David; Kumar, Vijay 
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7 Citations
Highly Influential Citations
1
Background Citations
4
Methods Citations
1

7 Citations

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This work learns a neural net that encodes the k most likely outcomes from high level actions from a given world, and creates comprehensible task plans that allow us to predict changes to the environment many time steps into the future.

Learning Long-term Dependencies with Deep Memory States

A reinforcement learning method is proposed that addresses the limitations of truncated BPTT by using a learned critic to estimate truncated gradients and by saving and loading hidden states outputted by recurrent neural networks.

CRLB Analysis for a Robust TRN Based on a Combination of RNN and PF

A robust PF-based TRN was designed, which uses a recurrent neural network (RNN)-based deep learning method to function on flat and repetitive terrains and a Cramér–Rao lower bound (CRLB) analysis was performed to evaluate how close the proposed method was to the optimal design.

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