- Benjie Holson
- Fei Xia
- Jeffrey Bingham
- Jie Tan
- Jonathan Weisz
- Mario Prats
- Montse Gonzalez Arenas
- Peng Xu
- Sumeet Singh
- Thomas Lew
- Tingnan Zhang
- Vikas Sindhwani
- Xiaohan Zhang
- Yao Lu
Abstract
We propose an end-to-end framework to enablemultipurpose assistive mobile robots to autonomously wipetables and clean spills and crumbs. This problem is chal-lenging, as it requires planning wiping actions with uncertainlatent crumbs and spill dynamics over high-dimensional visualobservations, while simultaneously guaranteeing constraintssatisfaction to enable deployment in unstructured environments.To tackle this problem, we first propose a stochastic differentialequation (SDE) to model crumbs and spill dynamics and ab-sorption with the robot wiper. Then, we formulate a stochasticoptimal control for planning wiping actions over visual obser-vations, which we solve using reinforcement learning (RL). Wethen propose a whole-body trajectory optimization formulationto compute joint trajectories to execute wiping actions whileguaranteeing constraints satisfaction. We extensively validateour table wiping approach in simulation and on hardware.
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