Learning Robot Trajectories subject to Kinematic Joint Constraints

TL;DR

This paper introduces a reinforcement learning-based method for generating fast, dynamic robot trajectories that strictly adhere to joint kinematic constraints by explicitly calculating safe accelerations, with the prediction frequency as a key hyperparameter.

Contribution

The paper presents an analytical procedure for computing safe joint accelerations considering neural network prediction frequency, enabling explicit safety guarantees during robot motion learning.

Findings

The approach ensures joint safety constraints are never violated.

Prediction frequency significantly impacts learning performance and computational effort.

Explicit acceleration calculations outperform penalty-based constraint methods.

Abstract

We present an approach to learn fast and dynamic robot motions without exceeding limits on the position $\theta$, velocity $\dot{\theta}$, acceleration $\ddot{\theta}$ and jerk $\dddot{\theta}$ of each robot joint. Movements are generated by mapping the predictions of a neural network to safely executable joint accelerations. The neural network is invoked periodically and trained via reinforcement learning. Our main contribution is an analytical procedure for calculating safe joint accelerations, which considers the prediction frequency $f_N$ of the neural network. As a result, the frequency $f_N$ can be freely chosen and treated as a hyperparameter. We show that our approach is preferable to penalizing constraint violations as it provides explicit guarantees and does not distort the desired optimization target. In addition, the influence of the selected prediction frequency on the learning performance and on the computing effort is highlighted by various experiments.