access icon free Generating Basic Unit Movements with Conditional Generative Adversarial Networks

Arm motion control is fundamental for robot accomplishing complicated manipulation tasks. Different movements can be organized by configuring a series of motion units. Our work aims at equipping the robot with the ability to carry out Basic unit movements (BUMs), which are used to constitute various motion sequences so that the robot can drive its hand to a desired position. With the definition of BUMs, we explore a learning approach for the robot to develop such an ability by leveraging deep learning technique. In order to generate the BUM regarding to the current arm state, an internal inverse model is developed. We propose to use Conditional generative adversarial networks (CGANs) to establish the inverse model to generate the BUMs. The experimental results on a humanoid robot PKU-HR6.0II illustrate that CGANs could successfully generate multiple solutions given a BUM, and these BUMs can be used to constitute further reaching movement effectively.

Inspec keywords: manipulators; robot vision; learning (artificial intelligence); humanoid robots; mobile robots; position control; motion control

Other keywords: motion sequences; manipulation tasks; arm motion control; BUM; humanoid robot PKU-HR6.0II; generating basic unit movements; deep learning technique; reaching movement; conditional generative adversarial networks; internal inverse model; current arm state

Subjects: Manipulators; Spatial variables control; Knowledge engineering techniques; Robot and manipulator mechanics; Mobile robots; Optical, image and video signal processing; Control engineering computing; Computer vision and image processing techniques

http://iet.metastore.ingenta.com/content/journals/10.1049/cje.2019.07.013
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