Robust Jumping with an Articulated Soft Quadruped via Trajectory Optimization and Iterative Learning

Journal article (2023)

Authors

J. Ding Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Franco Angelini University of Pisa
J. Kober Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
C. Della Santina Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 J. Ding, Mees A.van Loben Sels, Franco Angelini, J. Kober, C. Della Santina
DOI
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https://doi.org/10.1109/LRA.2023.3331288
Morphology Robots Dynamics Motion Control Trajectory optimization Legged Robots Springs Feedforward systems Optimization and Optimal Control Quadrupedal robots
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Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

Quadrupeds deployed in real-world scenarios need to be robust to unmodelled dynamic effects. In this work, we aim to increase the robustness of quadrupedal periodic forward jumping (i.e., pronking) by unifying cutting-edge model-based trajectory optimization and iterative learning control. Using a reduced-order soft anchor model, the optimization-based motion planner generates the periodic reference trajectory. The controller then iteratively learns the feedforward control signal in a repetition process, without requiring an accurate full-body model. When enhanced by a continuous learning mechanism, the proposed controller can learn the control inputs without resetting the system at the end of each iteration. Simulations and experiments on a quadruped with parallel springs demonstrate that continuous jumping can be learned in a matter of minutes, with high robustness against various types of terrain.