Spatial Action Maps for Mobile Manipulation

@article{Wu2020SpatialAM,
  title={Spatial Action Maps for Mobile Manipulation},
  author={Jimmy Wu and Xingyuan Sun and Andy Zeng and Shuran Song and Johnny Lee and Szymon M. Rusinkiewicz and Thomas A. Funkhouser},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.09141}
}
Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e.g., step forward, turn left, turn right, etc.) from images of the current state (e.g., a bird's-eye view of a SLAM reconstruction). Instead, we show that it can be advantageous to learn with dense action representations defined in the same domain as the state. In this work, we present "spatial action maps," in which the set of possible actions is represented by a pixel… 

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