mlsanityData to 14 Oct 2026

Deep Reinforcement Learning-Enhanced Event-Triggered Data-Driven Predictive Control for a 3D Cable-Driven Soft Robotic Arm

arXiv 2606.26048 ↗First posted 24 Jun 2026cs.RO cs.SY eess.SYcited by 0

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

Abstract (arXiv)

Soft robots are challenging to control due to their nonlinear and time-varying dynamics. Data-enabled predictive control (DeePC) offers a model-free alternative by directly leveraging measured input-output trajectories to construct a predictive controller. However, its receding-horizon formulation requires solving a constrained optimization problem at every sampling instant, which can be computationally demanding for real-time deployment on resource-limited robotic platforms. To address this limitation, we propose an adaptive reinforcement-learning-based event-triggered DeePC (RL-ET-DeePC) framework for soft robotic control. A model-free RL policy is trained to determine when to invoke the DeePC optimizer based on the current system state representation, thereby reducing unnecessary optimization calls while preserving closed-loop performance. Simulation results show that RL-ET-DeePC reduces optimization frequency by up to 66% compared to periodic DeePC, while maintaining comparable tracking accuracy. Hardware experiments on a three-dimensional cable-driven soft robotic arm demonstrate zero-shot transfer, achieving a 34% reduction in optimization frequency with tracking accuracy comparable to periodic DeePC and more consistent performance than a static threshold-based event-triggered baseline.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 27 references that are in the map. First posted on arXiv 24 Jun 2026.

Builds on

9 of 27 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016OpenAI Gymcited by 1,736
  3. 2018Data-Enabled Predictive Control: In the Shallows of the DeePCcited by 38
  4. 2021Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challengescited by 20
  5. 2022Event-Triggered Model Predictive Control with Deep Reinforcement Learning for Autonomous Drivingcited by 7
  6. 2024Design and Nonlinear Modeling of a Modular Cable Driven Soft Robotic Armcited by 2
  7. 2025Koopman-Based Event-Triggered Control from Datacited by 1
  8. 2025Velocity-Form Data-Enabled Predictive Control of Soft Robots under Unknown External Payloadscited by 1
  9. 2025Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Armcited by 1

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  1. 2024Reinforcement Learning Controllers for Soft Robots using Learned Environments
  2. 2025Real-Time Reinforcement Learning for Dynamic Tasks with a Parallel Soft Robot
  3. 2025Learning Soft Robotic Dynamics with Active Exploration
  4. 2025Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators
  5. 2026Generalizing deep reinforcement learning across cable-driven parallel robot configurations with actuator-level policies
  6. 2024An Efficient Learning Control Framework With Sim-to-Real for String-Type Artificial Muscle-Driven Robotic Systems
  7. 2025Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
  8. 2025Zero-shot Sim-to-Real Transfer for Reinforcement Learning-based Visual Servoing of Soft Continuum Arms