Autonomous Robotic Platform Training on Behavioral Cloning Neural Networks using ROS and VESC Project Resources

Urriza-Arellano, Bernardo A.; Cortes-Gallardo, Edgar; Bustamante-Bello, Rogelio; Rivera-Corona, Antonio C.; Rodríguez-Tirado, Areli; Tena-Padilla, Christian

doi:10.1109/icmeae.2019.00015

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Teaching robots by human demonstration allows people without programming skills and expertise in the control area to program robots by teaching behaviors Qian et al (2022) or cloning behaviors Bain and Sammut (1999a) Urriza-Arellano et al (2019). Learning from demonstration (LfD) is a machine learning technique that enables robots to autonomously learn and imitate tasks from the observations of human demonstration Sena and Howard (2020) Xu et al (2019) Wang et al (2021) Coates et al (2008).…”

Section: Introductionmentioning

confidence: 99%

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

Triana,

Arboleda,

de Oliveira

2024

Preprint

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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 success rate of 61.81% 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.

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