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

arXiv: https://arxiv.org/abs/2606.26048 · HTML: https://mlsanity.com/paper/2606.26048/

## 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.

## 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, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Data-Enabled Predictive Control: In the Shallows of the DeePC](https://mlsanity.com/paper/1811.05890/) (2018)
- [Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges](https://mlsanity.com/paper/2110.01358/) (2021)
- [Event-Triggered Model Predictive Control with Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2208.10302/) (2022)
- [Design and Nonlinear Modeling of a Modular Cable Driven Soft Robotic Arm](https://mlsanity.com/paper/2401.06377/) (2024)
- [Koopman-Based Event-Triggered Control from Data](https://mlsanity.com/paper/2504.14334/) (2025)
- [Velocity-Form Data-Enabled Predictive Control of Soft Robots under Unknown External Payloads](https://mlsanity.com/paper/2510.04509/) (2025)
- [Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Arm](https://mlsanity.com/paper/2510.08953/) (2025)

## 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.