# Learning-based Control for Tendon-Driven Continuum Robotic Arms

arXiv: https://arxiv.org/abs/2412.04829 · HTML: https://mlsanity.com/paper/2412.04829/

## 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 2 of its 32 references that are in the map.
- First posted on arXiv 6 Dec 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, 2 of 2)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)

## Abstract (arXiv)

This paper presents a learning-based approach for centralized position control of Tendon Driven Continuum Robots (TDCRs) using Deep Reinforcement Learning (DRL), with a particular focus on the Sim-to-Real transfer of control policies. The proposed control method employs the Modified Transpose Jacobian (MTJ) control strategy, with its parameters optimally tuned using the Deep Deterministic Policy Gradient (DDPG) algorithm. Classical model-based controllers encounter significant challenges due to the inherent uncertainties and nonlinear dynamics of continuum robots. In contrast, model-free control strategies require efficient gain-tuning to handle diverse operational scenarios. This research aims to develop a model-free controller with performance comparable to model-based strategies by integrating an optimal adaptive gain-tuning system. Both simulations and real-world implementations demonstrate that the proposed method significantly enhances the trajectory-tracking performance of continuum robots independent of initial conditions and paths within the operational task-space, effectively establishing a task-free controller.