# A Solution to Adaptive Mobile Manipulator Throwing

arXiv: https://arxiv.org/abs/2207.10629 · HTML: https://mlsanity.com/paper/2207.10629/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [Learning to Throw-Flip](https://mlsanity.com/paper/2510.10357/) (2025)
- [Learning to Throw Objects Safely in Multi-Obstacle Environments](https://mlsanity.com/paper/2607.06388/) (2026)
- [Non-Prehensile Throwing: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2609.00771/) (2026)

## Abstract (arXiv)

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.