Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain. (arXiv:2112.04684v2 [cs.RO] UPDATED)

We present a reward-predictive, model-based deep learning method featuring
trajectory-constrained visual attention for use in mapless, local visual
navigation tasks. Our method learns to place visual attention at locations in
latent image space which follow trajectories caused by vehicle control actions
to enhance predictive accuracy during planning. The attention model is jointly
optimized by the task-specific loss and an additional trajectory-constraint
loss, allowing adaptability yet encouraging a regularized structure for
improved generalization and reliability. Importantly, visual attention is
applied in …

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