Soft robotics promises to achieve safe and efficient interactions with the environment by exploiting its inherent compliance and designing control strategies. However, effective control for the soft robot-environment interaction has been a challenging task. The challenges arise from the nonlinearity and complexity of soft robot dynamics, especially in situations where the environment is unknown and uncertainties exist, making it difficult to establish analytical models. In this study, we propose a learning-based optimal control approach as an attempt to address these challenges, which is an optimized combination of a feedforward controller based on probabilistic model predictive control and a feedback controller based on nonparametric learning methods. The approach is purely data-driven, without prior knowledge of soft robot dynamics and environment structures, and can be easily updated online to adapt to unknown environments. A theoretical analysis of the approach is provided to ensure its stability and convergence. The proposed approach enabled a soft robotic manipulator to track target positions and forces when interacting with a manikin in different cases. Moreover, comparisons with other data-driven control methods show a better performance of our approach. Overall, this work provides a viable learning-based control approach for soft robot-environment interactions with force/position tracking capability.

中文翻译：

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基于学习的控制，用于具有力/位置跟踪功能的软机器人-环境交互。


Soft Robotics 承诺通过利用其固有的合规性和设计控制策略来实现与环境的安全高效交互。然而，有效控制软机器人与环境的交互一直是一项具有挑战性的任务。挑战来自软机器人动力学的非线性和复杂性，尤其是在环境未知且存在不确定性的情况下，难以建立分析模型。在这项研究中，我们提出了一种基于学习的最优控制方法，以尝试解决这些挑战，它是基于概率模型预测控制的前馈控制器和基于非参数学习方法的反馈控制器的优化组合。该方法完全由数据驱动，无需事先了解软机器人动力学和环境结构，并且可以轻松地在线更新以适应未知环境。为保证该方法的稳定性和收敛性，对该方法进行了理论分析。所提出的方法使软机器人机械手能够在不同情况下与人体模型交互时跟踪目标位置和力。此外，与其他数据驱动的控制方法的比较表明，我们的方法性能更好。总体而言，这项工作为具有力/位置跟踪能力的软机器人-环境交互提供了一种可行的基于学习的控制方法。