mlsanityData to 14 Oct 2026

Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators

arXiv 2506.08639 ↗First posted 10 Jun 2025cs.RO cs.SY eess.SYcited by 2

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

Abstract (arXiv)

This article presents a motion planning and control framework for flexible robotic manipulators, integrating deep reinforcement learning (DRL) with a nonlinear partial differential equation (PDE) controller. Unlike conventional approaches that focus solely on control, we demonstrate that the desired trajectory significantly influences endpoint vibrations. To address this, a DRL motion planner, trained using the soft actor-critic (SAC) algorithm, generates optimized trajectories that inherently minimize vibrations. The PDE nonlinear controller then computes the required torques to track the planned trajectory while ensuring closed-loop stability using Lyapunov analysis. The proposed methodology is validated through both simulations and real-world experiments, demonstrating superior vibration suppression and tracking accuracy compared to traditional methods. The results underscore the potential of combining learning-based motion planning with model-based control for enhancing the precision and stability of flexible robotic manipulators.

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 6 of its 28 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

6 of 28 references.

References · 6

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  3. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  4. 2019A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithmscited by 142
  5. 2023On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfercited by 26
  6. 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Taskscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Operator-Theoretic Foundations and Policy Gradient Methods for General MDPs with Unbounded Costscited by 1
  2. 2026Deep Reinforcement Learning for Adaptive Gain Tuning in Control of Teleoperation Manipulators with Joint Flexibility and Time-Varying Delayscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Reinforcement Learning Controllers for Soft Robots using Learned Environments
  2. 2020Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks with Base Controllers
  3. 2024Learning-based Control for Tendon-Driven Continuum Robotic Arms
  4. 2025Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
  5. 2024Combining Deep Reinforcement Learning with a Jerk-Bounded Trajectory Generator for Kinematically Constrained Motion Planning
  6. 2025Real-Time Reinforcement Learning for Dynamic Tasks with a Parallel Soft Robot
  7. 2024An Efficient Learning Control Framework With Sim-to-Real for String-Type Artificial Muscle-Driven Robotic Systems
  8. 2024Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL