Learning to Avoid Obstacles With Minimal Intervention Control

Duan, Anqing and Camoriano, Raffaello and Ferigo, Diego and Huang, Yanlong and Calandriello, Daniele and Rosasco, Lorenzo and Pucci, Daniele (2020) Learning to Avoid Obstacles With Minimal Intervention Control. Frontiers in Robotics and AI, 7. ISSN 2296-9144

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Abstract

Programming by demonstration has received much attention as it offers a general framework which allows robots to efficiently acquire novel motor skills from a human teacher. While traditional imitation learning that only focuses on either Cartesian or joint space might become inappropriate in situations where both spaces are equally important (e.g., writing or striking task), hybrid imitation learning of skills in both Cartesian and joint spaces simultaneously has been studied recently. However, an important issue which often arises in dynamical or unstructured environments is overlooked, namely how can a robot avoid obstacles? In this paper, we aim to address the problem of avoiding obstacles in the context of hybrid imitation learning. Specifically, we propose to tackle three subproblems: (i) designing a proper potential field so as to bypass obstacles, (ii) guaranteeing joint limits are respected when adjusting trajectories in the process of avoiding obstacles, and (iii) determining proper control commands for robots such that potential human-robot interaction is safe. By solving the aforementioned subproblems, the robot is capable of generalizing observed skills to new situations featuring obstacles in a feasible and safe manner. The effectiveness of the proposed method is validated through a toy example as well as a real transportation experiment on the iCub humanoid robot.

Item Type: Article
Subjects: Souths Book > Mathematical Science
Depositing User: Unnamed user with email support@southsbook.com
Date Deposited: 29 Jun 2023 05:20
Last Modified: 24 Jul 2024 09:58
URI: http://research.europeanlibrarypress.com/id/eprint/1321

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