10.3389/fnbot.2019.00077.s001 J. Camilo Vasquez Tieck J. Camilo Vasquez Tieck Tristan Schnell Tristan Schnell Jacques Kaiser Jacques Kaiser Felix Mauch Felix Mauch Arne Roennau Arne Roennau RĂ¼diger Dillmann RĂ¼diger Dillmann Video_1_Generating Pointing Motions for a Humanoid Robot by Combining Motor Primitives.MP4 Frontiers 2019 neurorobotics motion generation spiking neural networks (SNN) pointing a target motor primitives humanoid robot (HR) closed-loop 2019-09-18 10:23:24 Media https://frontiersin.figshare.com/articles/media/Video_1_Generating_Pointing_Motions_for_a_Humanoid_Robot_by_Combining_Motor_Primitives_MP4/9873386 <p>The human motor system is robust, adaptive and very flexible. The underlying principles of human motion provide inspiration for robotics. Pointing at different targets is a common robotics task, where insights about human motion can be applied. Traditionally in robotics, when a motion is generated it has to be validated so that the robot configurations involved are appropriate. The human brain, in contrast, uses the motor cortex to generate new motions reusing and combining existing knowledge before executing the motion. We propose a method to generate and control pointing motions for a robot using a biological inspired architecture implemented with spiking neural networks. We outline a simplified model of the human motor cortex that generates motions using motor primitives. The network learns a base motor primitive for pointing at a target in the center, and four correction primitives to point at targets up, down, left and right from the base primitive, respectively. The primitives are combined to reach different targets. We evaluate the performance of the network with a humanoid robot pointing at different targets marked on a plane. The network was able to combine one, two or three motor primitives at the same time to control the robot in real-time to reach a specific target. We work on extending this work from pointing to a given target to performing a grasping or tool manipulation task. This has many applications for engineering and industry involving real robots.</p>