A Solution to Adaptive Mobile Manipulator Throwing

Yang Liu, Aradhana Nayak, and Aude Billard
Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS)


Mobile manipulator throwing is a promising method to increase the flexibility and efficiency of dynamic manipulation in factories. Its major challenge is to efficiently plan a feasible throw under a wide set of task specifications. We show that the mobile manipulator throwing problem can be simplified to a planar problem, hence greatly reducing the
computational costs. Using machine learning approaches, we build a model of the object’s inverted flying dynamics and the robot’s kinematic feasibility, which enables throwing motion generation within 1 ms for given query of target position. Thanks to the computational efficiency of our method, we show that the system is adaptive under disturbance, via replanning on the fly for alternative solutions, instead of sticking to the original throwing plan.

	title = {A Solution to Adaptive Mobile Manipulator Throwing},
	author = {Liu, Yang and Nayak, Aradhana and Billard, Aude},