Generating Pointing Motions for a Humanoid Robot by Combining Motor Primitives

Abstract

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.

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

“…A robot based on the Q-Learning algorithm only knows the set of actions that can be selected at the moment. To represent the action reward value from one to the next state, a corresponding matrix is usually constructed, from which the Q matrix that can guide the robot's activities is obtained (Hung et al, 2018 ; Tieck et al, 2019 ; Wong et al, 2019 ).…”

Soft-Grasping With an Anthropomorphic Robotic Hand Using Spiking Neurons

“…The first diagram shows the activation of the control triggered by the contact detection. If a contact is detected, the inhibitory neurons are inhibited (selective desinhibition [35]) and the control is activated. The plot u correction is the signal added by the adaptive control to the PI control.…”

“…An stereo even-based camera setup [45] can be used to get the target point is 3D space for pre-grasping [46] and use micro-saccades [47] to detect the type of object and identify which grasping affordance to use. To integrate arm motion, an arm controller as in [48] can be incorporated to do visual servoing, or as in [35] to have primitives reach different points on a surface.The combination of visual information and arm motion, together with soft-grasping can achieve a more natural grasping process from recognition of the object to positioning the arm to grasping. The whole system implemented completely with SNN as physical imitation of a biological system and an anthropomorphic robotic hand can be compared to brain neural responses for the grasping process as shown by [15] and provide new insights into its sub-processes.…”

A Computational Framework of Goal Directed Voluntary Motion Generation and Control Loop in Humanoid Robots

“…Tieck et al presented a procedure creating pointing behaviors for a robot with spiking neural model-based architecture [24]. They depicted a basic model of the human engine cortex producing movements utilizing motor functions.…”