# Catch the Ball: Accurate High-Speed Motions for Mobile Manipulators via Inverse Dynamics Learning

arXiv: https://arxiv.org/abs/2003.07489 · HTML: https://mlsanity.com/paper/2003.07489/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022)
- [Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning](https://mlsanity.com/paper/2210.04435/) (2022)
- [Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control](https://mlsanity.com/paper/2302.13796/) (2023)
- [Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control](https://mlsanity.com/paper/2504.17771/) (2025)
- [DexDribbler: Learning Dexterous Soccer Manipulation via Dynamic Supervision](https://mlsanity.com/paper/2403.14300/) (2024)
- [Modular Neural Network Policies for Learning In-Flight Object Catching with a Robot Hand-Arm System](https://mlsanity.com/paper/2312.13987/) (2023)
- [Spatiotemporal Agility: Time-Constrained Reinforcement Learning for Vision-Guided Dynamic Quadrupedal Interception](https://mlsanity.com/paper/2608.06907/) (2026)

## Abstract (arXiv)

Mobile manipulators consist of a mobile platform equipped with one or more robot arms and are of interest for a wide array of challenging tasks because of their extended workspace and dexterity. Typically, mobile manipulators are deployed in slow-motion collaborative robot scenarios. In this paper, we consider scenarios where accurate high-speed motions are required. We introduce a framework for this regime of tasks including two main components: (i) a bi-level motion optimization algorithm for real-time trajectory generation, which relies on Sequential Quadratic Programming (SQP) and Quadratic Programming (QP), respectively; and (ii) a learning-based controller optimized for precise tracking of high-speed motions via a learned inverse dynamics model. We evaluate our framework with a mobile manipulator platform through numerous high-speed ball catching experiments, where we show a success rate of 85.33%. To the best of our knowledge, this success rate exceeds the reported performance of existing related systems and sets a new state of the art.