# Combining Deep Reinforcement Learning with a Jerk-Bounded Trajectory Generator for Kinematically Constrained Motion Planning

arXiv: https://arxiv.org/abs/2410.20907 · HTML: https://mlsanity.com/paper/2410.20907/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 17 of 17)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Safe Model-based Reinforcement Learning with Stability Guarantees](https://mlsanity.com/paper/1705.08551/) (2017)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [State-wise Safe Reinforcement Learning: A Survey](https://mlsanity.com/paper/2302.03122/) (2023)
- [A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight](https://mlsanity.com/paper/2202.10796/) (2022)
- [Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking](https://mlsanity.com/paper/2205.06750/) (2022)
- [On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer](https://mlsanity.com/paper/2312.03673/) (2023)
- [Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning](https://mlsanity.com/paper/2111.07171/) (2021)
- [Joint Space Control via Deep Reinforcement Learning](https://mlsanity.com/paper/2011.06332/) (2020)
- [Learning Robot Trajectories subject to Kinematic Joint Constraints](https://mlsanity.com/paper/2011.00563/) (2020)
- [Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors](https://mlsanity.com/paper/2103.03793/) (2021)
- [Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks](https://mlsanity.com/paper/2402.02551/) (2024)
- [Analytical Forward Dynamics Modeling of Linearly Actuated Heavy-Duty Parallel-Serial Manipulators](https://mlsanity.com/paper/2403.08524/) (2024)

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

- [Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies](https://mlsanity.com/paper/2609.13011/) (2026)
- [PAKT: Physically-Aligned Kinesthetic Teaching for Reinforcement Learning](https://mlsanity.com/paper/2609.25630/) (2026)

## Abstract (arXiv)

Deep reinforcement learning (DRL) is emerging as a promising method for adaptive robotic motion and complex task automation, effectively addressing the limitations of traditional control methods. However, ensuring safety throughout both the learning process and policy deployment remains a key challenge due to the risky exploration inherent in DRL, as well as the discrete nature of actions taken at intervals. These discontinuities, despite being part of a continuous action space, can lead to abrupt changes between successive actions, causing instability and unsafe intermediate states. To address these challenges, this paper proposes an integrated framework that combines DRL with a jerk-bounded trajectory generator (JBTG) and a robust low-level control strategy, significantly enhancing the safety, stability, and reliability of robotic manipulators. The low-level controller ensures the precise execution of DRL-generated commands, while the JBTG refines these motions to produce smooth, continuous trajectories that prevent abrupt or unsafe actions. The framework also includes pre-calculated safe velocity zones for smooth braking, preventing joint limit violations and ensuring compliance with kinematic constraints. This approach not only guarantees the robustness and safety of the robotic system but also optimizes motion control, making it suitable for practical applications. The effectiveness of the proposed framework is demonstrated through its application to a highly complex heavy-duty manipulator.