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 that
our method safely and reliably steers the system to the goal, while baselines
that fail to consider the trusted domain or state estimation errors can be
unsafe.

本研究提出了一种针对不确定控制仿射非线性系统的运动规划算法，在使用高维传感器测量（如 RGB-D 图像）和一个学习感知模块的反馈控制环路时保证运行时安全性和目标可达性。