Dynamic Motion Primitives (DMPs) only address the generalization problem for target positions that are close to the demonstration position and in order to enhance the generalization capability, by making the learned movements executable in the entire reachable robot workspace, multiple demonstrations are needed, resulting in an increase in the time required to teach new tasks to the robot. This work aims to propose a novel approach to scale the DMP parameters through two demonstrations in order to enhance the DMP's generalization capability in the robot reachable workspace while guaranteeing a fast and easy learning phase. The proposed method to scale the DMP parameters relies on a linear interpolation performed on the DMP parameters extracted by two demonstrations and is applied to the agricultural field, where the adoption of DMPs can provide a promising solution to meet the demand for a wide range of tasks in an ever-changing and mutable agricultural environment. Four agricultural activities, namely digging, seeding, irrigation, and harvesting, have been learned by the robot using DMPs. The experimental validation was carried out on the Tiago robot and the proposed approach, based on two demonstrations, was compared to two literature methods based on a single demonstration and multiple demonstrations in terms of accuracy of the motion reconstruction, success rate of task execution and speed of the learning process. The obtained results demonstrated that the proposed approach, based on two demonstrations, guarantees a successful execution of the four tasks without exceeding the robot reachable workspace and with acceptable accuracy (mean success rate of task execution is about 95.6%) and a fast training phase (about 70 minutes less than the database approach that is built on multiple demonstrations).

A New DMP Scaling Method for Robot Learning by Demonstration and Application to the Agricultural Domain

Lauretti, C.
;
Tamantini, C.;Zollo, L.
2024-01-01

Abstract

Dynamic Motion Primitives (DMPs) only address the generalization problem for target positions that are close to the demonstration position and in order to enhance the generalization capability, by making the learned movements executable in the entire reachable robot workspace, multiple demonstrations are needed, resulting in an increase in the time required to teach new tasks to the robot. This work aims to propose a novel approach to scale the DMP parameters through two demonstrations in order to enhance the DMP's generalization capability in the robot reachable workspace while guaranteeing a fast and easy learning phase. The proposed method to scale the DMP parameters relies on a linear interpolation performed on the DMP parameters extracted by two demonstrations and is applied to the agricultural field, where the adoption of DMPs can provide a promising solution to meet the demand for a wide range of tasks in an ever-changing and mutable agricultural environment. Four agricultural activities, namely digging, seeding, irrigation, and harvesting, have been learned by the robot using DMPs. The experimental validation was carried out on the Tiago robot and the proposed approach, based on two demonstrations, was compared to two literature methods based on a single demonstration and multiple demonstrations in terms of accuracy of the motion reconstruction, success rate of task execution and speed of the learning process. The obtained results demonstrated that the proposed approach, based on two demonstrations, guarantees a successful execution of the four tasks without exceeding the robot reachable workspace and with acceptable accuracy (mean success rate of task execution is about 95.6%) and a fast training phase (about 70 minutes less than the database approach that is built on multiple demonstrations).
2024
Learning by demonstration; teaching by demonstration; robot learning; dynamic motion primitives; motion planning; agricultural robotics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/77670
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