Adaptive Asynchronous Control Using Meta-Learned Neural Ordinary Differential Equations

Model-based reinforcement learning and control have demonstrated great potential in various sequential decision making problem domains, including in robotics settings. However, real-world robotics systems often present challenges that limit the applicability of those methods. In particular, we note...

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Veröffentlicht in:IEEE transactions on robotics Jg. 40; S. 403 - 420
Hauptverfasser: Salehi, Achkan, Ruhl, Steffen, Doncieux, Stephane
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive control
Adaptive systems
Computer Science
Differential equations
Industrial robots
learning and adaptive systems
Machine Learning
Metalearning
model learning for control
Reinforcement learning
Robot control
Robot learning
Robotics
robust/adaptive control of robotic systems
Service robots
Industrial robots
Model Learning for Control
learning and adaptive systems
Robust/Adaptive Control of Robotic Systems
ISSN:1552-3098, 1941-0468
Online-Zugang:Volltext
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Zusammenfassung:Model-based reinforcement learning and control have demonstrated great potential in various sequential decision making problem domains, including in robotics settings. However, real-world robotics systems often present challenges that limit the applicability of those methods. In particular, we note two problems that jointly happen in many industrial systems: first, irregular/asynchronous observations and actions and, second, dramatic changes in environment dynamics from an episode to another (e.g .<inline-formula><tex-math notation="LaTeX">,</tex-math></inline-formula> varying payload inertial properties). We propose a general framework that overcomes those difficulties by meta-learning adaptive dynamics models for continuous-time prediction and control. The proposed approach is task-agnostic and can be adapted to new tasks in a straight-forward manner. We present evaluations in two different robot simulations and on a real industrial robot.
Bibliographie:ObjectType-Article-1
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2023.3326350