  We propose a framework to enable multipurpose assistive mobile robots to
autonomously wipe tables to clean spills and crumbs. This problem is
challenging, as it requires planning wiping actions while reasoning over
uncertain latent dynamics of crumbs and spills captured via high-dimensional
visual observations. Simultaneously, we must guarantee constraints satisfaction
to enable safe deployment in unstructured cluttered environments. To tackle
this problem, we first propose a stochastic differential equation to model
crumbs and spill dynamics and absorption with a robot wiper. Using this model,
we train a vision-based policy for planning wiping actions in simulation using
reinforcement learning (RL). To enable zero-shot sim-to-real deployment, we
dovetail the RL policy with a whole-body trajectory optimization framework to
compute base and arm joint trajectories that execute the desired wiping motions
while guaranteeing constraints satisfaction. We extensively validate our
approach in simulation and on hardware. Video: https://youtu.be/inORKP4F3EI

Robotics;Artificial Intelligence;Machine Learning;Systems and Control;Systems and Control