A Fast and Reliable Pick-and-Place Application with a Spherical Soft Robotic Arm

Zughaibi, Jasan; Hofer, Matthias; D’Andrea, Raffaello

doi:10.1109/robosoft51838.2021.9479227

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Due to the high nonlinearity of PAMs, Hofer et al [15] and Zughaibi et al [34] proposed the use of ILC to improve the position tracking performance for an articulated soft robotic arm during aggressive maneuvers. ILC is a learning control method, which stands for the repeatability of operating a given system and the possibility of updating the control input based on previous operation data to improve the transient performance of systems over a fixed time interval, [3,6,1].…”

Section: A Related Workmentioning

confidence: 99%

A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles

Ma¹,

B�chler²,

Sch�lkopf³

et al. 2022

Robotics: Science and Systems XVIII

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In this work, we propose a new learning-based iterative control (IC) framework that enables a complex softrobotic arm to track trajectories accurately. Compared to traditional iterative learning control (ILC), which operates on a single fixed reference trajectory, we use a deep learning approach to generalize across various reference trajectories. The resulting nonlinear mapping computes feedforward actions and is used in a two degrees of freedom control design. Our method incorporates prior knowledge about the system dynamics and by learning only feedforward actions, it mitigates the risk of instability. We demonstrate a low sample complexity and an excellent tracking performance in real-world experiments. The experiments are carried out on a custom-made robot arm with four degrees of freedom that is actuated with pneumatic artificial muscles. The experiments include high acceleration and high velocity motion.

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Section: A Related Workmentioning

confidence: 99%

A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles

Ma¹,

B�chler²,

Sch�lkopf³

et al. 2022

Robotics: Science and Systems XVIII

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Reinforcement learning with model-based feedforward inputs for robotic table tennis

Ma,

Büchler,

Schölkopf

et al. 2023

Auton Robot

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We rethink the traditional reinforcement learning approach, which is based on optimizing over feedback policies, and propose a new framework that optimizes over feedforward inputs instead. This not only mitigates the risk of destabilizing the system during training but also reduces the bulk of the learning to a supervised learning task. As a result, efficient and well-understood supervised learning techniques can be applied and are tuned using a validation data set. The labels are generated with a variant of iterative learning control, which also includes prior knowledge about the underlying dynamics. Our framework is applied for intercepting and returning ping-pong balls that are played to a four-degrees-of-freedom robotic arm in real-world experiments. The robot arm is driven by pneumatic artificial muscles, which makes the control and learning tasks challenging. We highlight the potential of our framework by comparing it to a reinforcement learning approach that optimizes over feedback policies. We find that our framework achieves a higher success rate for the returns ($$100\%$$ 100 % vs. $$96\%$$ 96 % , on 107 consecutive trials, see https://youtu.be/kR9jowEH7PY) while requiring only about one tenth of the samples during training. We also find that our approach is able to deal with a variant of different incoming trajectories.

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Spring-IMU Fusion-Based Proprioception for Feedback Control of Soft Manipulators

Meng,

Fang,

Yang

et al. 2024

IEEE/ASME Trans. Mechatron.

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A Fast and Reliable Pick-and-Place Application with a Spherical Soft Robotic Arm

Cited by 4 publications

References 23 publications

A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles

A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles

Reinforcement learning with model-based feedforward inputs for robotic table tennis

Spring-IMU Fusion-Based Proprioception for Feedback Control of Soft Manipulators

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