Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory. (arXiv:2206.06553v1 [cs.RO])

We present a motion planning algorithm for a class of uncertain
control-affine nonlinear systems which guarantees runtime safety and goal
reachability when using high-dimensional sensor measurements (e.g., RGB-D
images) and a learned perception module in the feedback control loop. First,
given a dataset of states and observations, we train a perception system that
seeks to invert a subset of the state from an observation, and estimate an
upper bound on the perception error which is valid with high probability in …

arxiv feedback images motion planning perception planning theory