Adaptive CLF-MPC with application to quadrupedal robots
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Author / Producer
Date
2022-01
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
Modern robotic systems are endowed with superior mobility and mechanical skills that make them suited to be employed in real-world scenarios, where interactions with heavy objects and precise manipulation capabilities are required. For instance, legged robots with high payload capacity can be used in disaster scenarios to remove dangerous material or carry injured people. It is thus essential to develop planning algorithms that can enable complex robots to perform motion and manipulation tasks accurately. In addition, online adaptation mechanisms with respect to new, unknown environments are needed. In this work, we impose that the optimal state-input trajectories generated by Model Predictive Control (MPC) satisfy the Lyapunov function criterion derived in adaptive control for robotic systems. As a result, we combine the stability guarantees provided by Control Lyapunov Functions (CLFs) and the optimality offered by MPC in a unified adaptive framework, yielding an improved performance during the robot’s interaction with unknown objects.
We validate the proposed approach in simulation and hardware tests on a quadrupedal robot carrying un-modeled payloads and pulling heavy boxes.
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Publication status
published
External links
Editor
Book title
Journal / series
Volume
7 (1)
Pages / Article No.
565 - 572
Publisher
IEEE
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
RSL; dfab; NCCR Robotics
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Notes
Funding
780883 - subTerranean Haptic INvestiGator (EC)
Related publications and datasets
Is cited by: 10.3929/ethz-b-000636009