A modified recurrent unit with an input-multiplied gate improves spatial memory and long-range mapless navigation success rates by about 23.5% over standard RNNs in simulation and transfers zero-shot to a real robot.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
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Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning
A modified recurrent unit with an input-multiplied gate improves spatial memory and long-range mapless navigation success rates by about 23.5% over standard RNNs in simulation and transfers zero-shot to a real robot.