# An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics

arXiv: https://arxiv.org/abs/1709.04407 · HTML: https://mlsanity.com/paper/1709.04407/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Privileged observations enable rapid and reliable policy discovery directly in the physical world](https://mlsanity.com/paper/2512.08463/) (2025)
- [Deep Q-Learning-Based Gain Scheduling for Nonlinear Quadcopter Dynamics](https://mlsanity.com/paper/2603.03127/) (2026)

## Abstract (arXiv)

This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i.e., systems with unstable inverse dynamics. Inversion-based feedforward approaches are commonly used for improving tracking performance; however, these approaches are not directly applicable to non-minimum phase systems due to their inherent instability. In order to resolve the instability issue, existing methods have assumed that the system model is known and used pre-actuation or inverse approximation techniques. In this work, we propose an approach for learning a stable, approximate inverse of a non-minimum phase baseline system directly from its input-output data. Through theoretical discussions, simulations, and experiments on two different platforms, we show the stability of our proposed approach and its effectiveness for high-accuracy, impromptu tracking. Our approach also shows that including more information in the training, as is commonly assumed to be useful, does not lead to better performance but may trigger instability and impact the effectiveness of the overall approach.