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Learning from Demonstration in Embedded Hardware for the Composition of Micro-Skills in Mobile Robots.

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Title: Learning from Demonstration in Embedded Hardware for the Composition of Micro-Skills in Mobile Robots.
Authors: Triana, Mario Andrés Pastrana1 (AUTHOR) mario.pastrana@ieee.org, de Oliveira, Luiz Henrique Nunes1 (AUTHOR) luiz.oliveira@ieee.org, Muñoz, Daniel M.1,2 (AUTHOR) damuz@unb.br
Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Mar2026, Vol. 51 Issue 5, p6387-6412. 26p.
Subject Terms: *Mobile robots, *Observational learning, *Motor ability, *Embedded computer systems, *Particle swarm optimization, *Artificial neural networks, *Obstacle avoidance (Robotics), *Robotic path planning
Abstract: Designing mobile robot controllers for non-expert users is a complex task that involves programming abilities and developing accurate mathematical models for representing the robot's kinematics, sensor observations, and parameter estimations. Learning from demonstration (LfD) is a technique that enables robots to autonomously learn and perform new tasks from the observations of human demonstrations. Obtaining models with low computational complexity is of utmost importance for embedded robotics systems. This paper proposes the usage of LfD for learning three simple micro-skills (move forward, turn clockwise, and turn counterclockwise) that can be combined to efficiently perform more complex skills on a mobile robot. An adaptive single-layer perceptron neural network (SLP) and a particle swarm optimization (PSO) training algorithm were implemented using a low-cost system-on-chip (SoC) device, allowing the robot to learn the wheel's speed profile for each demonstrated micro-skill and compose them to navigate around obstacles in complex scenarios. Real-world scenarios were used to statistically analyze the robustness of the proposed methodology, achieving a success rate of 100% for the known scenarios and a maximum success rate of 93.75% for the unknown scenarios. Experimental results demonstrated that the robot correctly moves on several unknown scenarios that compose the three taught micro-skills, avoiding obstacles and correcting deviations from the initial position. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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DbLabel: Energy & Power Source
An: 193141623
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Learning from Demonstration in Embedded Hardware for the Composition of Micro-Skills in Mobile Robots.
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  Data: *<searchLink fieldCode="DE" term="%22Mobile+robots%22">Mobile robots</searchLink><br />*<searchLink fieldCode="DE" term="%22Observational+learning%22">Observational learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Motor+ability%22">Motor ability</searchLink><br />*<searchLink fieldCode="DE" term="%22Embedded+computer+systems%22">Embedded computer systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Obstacle+avoidance+%28Robotics%29%22">Obstacle avoidance (Robotics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink>
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  Data: Designing mobile robot controllers for non-expert users is a complex task that involves programming abilities and developing accurate mathematical models for representing the robot's kinematics, sensor observations, and parameter estimations. Learning from demonstration (LfD) is a technique that enables robots to autonomously learn and perform new tasks from the observations of human demonstrations. Obtaining models with low computational complexity is of utmost importance for embedded robotics systems. This paper proposes the usage of LfD for learning three simple micro-skills (move forward, turn clockwise, and turn counterclockwise) that can be combined to efficiently perform more complex skills on a mobile robot. An adaptive single-layer perceptron neural network (SLP) and a particle swarm optimization (PSO) training algorithm were implemented using a low-cost system-on-chip (SoC) device, allowing the robot to learn the wheel's speed profile for each demonstrated micro-skill and compose them to navigate around obstacles in complex scenarios. Real-world scenarios were used to statistically analyze the robustness of the proposed methodology, achieving a success rate of 100% for the known scenarios and a maximum success rate of 93.75% for the unknown scenarios. Experimental results demonstrated that the robot correctly moves on several unknown scenarios that compose the three taught micro-skills, avoiding obstacles and correcting deviations from the initial position. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s13369-025-10699-5
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      – Code: eng
        Text: English
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        PageCount: 26
        StartPage: 6387
    Subjects:
      – SubjectFull: Mobile robots
        Type: general
      – SubjectFull: Observational learning
        Type: general
      – SubjectFull: Motor ability
        Type: general
      – SubjectFull: Embedded computer systems
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Obstacle avoidance (Robotics)
        Type: general
      – SubjectFull: Robotic path planning
        Type: general
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      – TitleFull: Learning from Demonstration in Embedded Hardware for the Composition of Micro-Skills in Mobile Robots.
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            NameFull: Triana, Mario Andrés Pastrana
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            NameFull: de Oliveira, Luiz Henrique Nunes
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            NameFull: Muñoz, Daniel M.
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            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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