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Article: Adaptive micro-locomotion in a dynamically changing environment via context detection
Title | Adaptive micro-locomotion in a dynamically changing environment via context detection |
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Authors | |
Issue Date | 1-Jul-2024 |
Publisher | Elsevier |
Citation | Communications in Nonlinear Science and Numerical Simulation, 2024, v. 128 How to Cite? |
Abstract | Substantial efforts have exploited reinforcement learning (RL) in the development of micro-robotic locomotion. These RL-powered micro-robots are capable of learning a locomotory policy based on their experience interacting with the surroundings, without requiring prior knowledge on the physics of locomotion in that environment. However, in their applications, micro-robots often encounter changes in the environment and need to adapt their locomotory gaits like living organisms in order to achieve robust locomotion performance. In standard RL methods, such a non-stationary environment can cause the micro-robots to continuously relearn the policy from scratch, degrading their locomotion performance. In this work, we explore a first use of a recently developed context detection method combined with deep RL to facilitate micro-robotic locomotion |
Persistent Identifier | http://hdl.handle.net/10722/339473 |
ISSN | 2023 Impact Factor: 3.4 2023 SCImago Journal Rankings: 0.919 |
DC Field | Value | Language |
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dc.contributor.author | Zou, Zonghao | - |
dc.contributor.author | Liu, Yuexin | - |
dc.contributor.author | Tsang, Alan CH | - |
dc.contributor.author | Young, Y-N | - |
dc.contributor.author | Pak, On Shun | - |
dc.date.accessioned | 2024-03-11T10:36:55Z | - |
dc.date.available | 2024-03-11T10:36:55Z | - |
dc.date.issued | 2024-07-01 | - |
dc.identifier.citation | Communications in Nonlinear Science and Numerical Simulation, 2024, v. 128 | - |
dc.identifier.issn | 1007-5704 | - |
dc.identifier.uri | http://hdl.handle.net/10722/339473 | - |
dc.description.abstract | <p>Substantial efforts have exploited reinforcement learning (RL) in the development of micro-robotic locomotion. These RL-powered micro-robots are capable of learning a locomotory policy based on their experience interacting with the surroundings, without requiring prior knowledge on the physics of locomotion in that environment. However, in their applications, micro-robots often encounter changes in the environment and need to adapt their locomotory gaits like living organisms in order to achieve robust locomotion performance. In standard RL methods, such a non-stationary environment can cause the micro-robots to continuously relearn the policy from scratch, degrading their locomotion performance. In this work, we explore a first use of a recently developed context detection method combined with deep RL to facilitate micro-robotic locomotion<br>in a dynamically changing environment. As a proof-of-principle, we consider a simple micro-robot immersed in non-stationary environments switching between a viscous fluid environment and a dry frictional environment. We show that the RL with context detection approach enables the micro-robot to effectively detect changes in the environment and deploy specialized locomotory gaits for different environments accordingly<br>to achieve significantly improved locomotion. Our results suggest the integration of deep RL with context detection as a potential tool for robust micro-robotic locomotion across different environments.</p> | - |
dc.language | eng | - |
dc.publisher | Elsevier | - |
dc.relation.ispartof | Communications in Nonlinear Science and Numerical Simulation | - |
dc.title | Adaptive micro-locomotion in a dynamically changing environment via context detection | - |
dc.type | Article | - |
dc.identifier.volume | 128 | - |
dc.identifier.eissn | 1878-7274 | - |
dc.identifier.issnl | 1007-5704 | - |