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Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory by Chou, Glen; Ozay, Necmiye; Berenson, Dmitry is a scholarly article available to read on EtoBox.

What is Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory about?

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 a trusted domain near the data. Next, we use contraction theory to design a stabilizing state feedback controller and a convergent dynamic state observer which uses the learned perception system to update its state estimate. We derive a bound on the trajectory tracking error when this controller is subjected to errors in the dynamics and incorrect state estimates. Finally, we integrate this bound into a sampling-based motion planner, guiding it to return trajectories that can be safely tracked at runtime using sensor data. We demonstrate our approach in simulation on a 4D car, a 6D planar quadrotor, and a 17D manipulation task with RGB(-D) sensor measurements, demonstrating

Author

Chou, Glen; Ozay, Necmiye; Berenson, Dmitry

Published

2022

Language

EN