mlsanityData to 14 Oct 2026

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

arXiv 2410.20907 ↗First posted 28 Oct 2024eess.SY cs.SYcited by 2

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

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.

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.

Builds on

17 of 29 references.

References · 17

  1. 2016OpenAI Gymcited by 1,736
  2. 2019Solving Rubik's Cube with a Robot Handcited by 734
  3. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  4. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
  5. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 366
  6. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  7. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 316
  8. 2023State-wise Safe Reinforcement Learning: A Surveycited by 55
  9. 2022A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flightcited by 47
  10. 2022Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarkingcited by 34
  11. 2023On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfercited by 26
  12. 2021Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuningcited by 24
5 more
  1. 2020Joint Space Control via Deep Reinforcement Learningcited by 7
  2. 2020Learning Robot Trajectories subject to Kinematic Joint Constraintscited by 5
  3. 2021Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviorscited by 4
  4. 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Taskscited by 3
  5. 2024Analytical Forward Dynamics Modeling of Linearly Actuated Heavy-Duty Parallel-Serial Manipulatorscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policiescited by 0
  2. 2026PAKT: Physically-Aligned Kinesthetic Teaching for Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach
  2. 2024Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL
  3. 2025Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators
  4. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
  5. 2026DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties
  6. 2025Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
  7. 2025A nonlinear real time capable motion cueing algorithm based on deep reinforcement learning
  8. 2026Safe Reinforcement Learning using Ideas from Model Predictive Control