用深度强化学习优化绳驱动机器臂控制，实现高精度无初始依赖轨迹跟踪。
Learning-based Control for Tendon-Driven Continuum Robotic Arms
- 基于DDPG算法自适应调优改进转置雅可比控制参数
- 仿真与实机验证均实现对任意路径的精准跟踪
- 无需依赖模型或初始条件，适合复杂任务场景
本文提出一种基于深度强化学习（DRL）的集中式位置控制方法，用于绳驱动连续体机器人（TDCRs），重点解决控制策略从仿真到现实的迁移问题。所提方法采用改进转置雅可比（MTJ）控制策略，并通过深度确定性策略梯度（DDPG）算法最优调参。传统基于模型的控制器因连续体机器人固有的不确定性与非线性动态而面临挑战，而无模型策略则需高效增益调优以应对多样操作场景。本研究旨在通过集成最优自适应增益调优系统，实现性能媲美基于模型策略的无模型控制器。仿真与真实实验均表明，该方法显著提升连续体机器人在任务空间内任意初始条件与路径下的轨迹跟踪性能，有效构建了无需任务依赖的控制器。
原文摘要 · Abstract (English)
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.
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