# Learning Robot Trajectories subject to Kinematic Joint Constraints

arXiv: https://arxiv.org/abs/2011.00563 · HTML: https://mlsanity.com/paper/2011.00563/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors](https://mlsanity.com/paper/2103.03793/) (2021)
- [Combining Deep Reinforcement Learning with a Jerk-Bounded Trajectory Generator for Kinematically Constrained Motion Planning](https://mlsanity.com/paper/2410.20907/) (2024)
- [Safe Reinforcement Learning of Robot Trajectories in the Presence of Moving Obstacles](https://mlsanity.com/paper/2411.05784/) (2024)
- [Learning Time-optimized Path Tracking with or without Sensory Feedback](https://mlsanity.com/paper/2203.01968/) (2022)
- [Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping](https://mlsanity.com/paper/2607.23930/) (2026)

## Abstract (arXiv)

We present an approach to learn fast and dynamic robot motions without exceeding limits on the position θ, velocity dotθ, acceleration ddotθ and jerk dddotθ 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 fN of the neural network. As a result, the frequency fN 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.