Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation

Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the...

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Vydané v:	IEEE transactions on robotics Ročník 39; číslo 4; s. 3259 - 3278
Hlavní autori:	Biagiotti, Luigi, Meattini, Roberto, Chiaravalli, Davide, Palli, Gianluca, Melchiorri, Claudio
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Constrained smoothing B-splines Education human–robot interaction Learning Muscles Optimization Programming programming by demonstration (PbD) Robots Splines (mathematics) Stiffness surface electromyography (sEMG) signals Task analysis Trajectory Trajectory analysis
ISSN:	1552-3098, 1941-0468
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Abstract	Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion.
AbstractList	Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion.
Author	Biagiotti, Luigi Meattini, Roberto Palli, Gianluca Chiaravalli, Davide Melchiorri, Claudio
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SubjectTerms	Constrained smoothing B-splines Education human–robot interaction Learning Muscles Optimization Programming programming by demonstration (PbD) Robots Splines (mathematics) Stiffness surface electromyography (sEMG) signals Task analysis Trajectory Trajectory analysis
Title	Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation
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