Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Prediction of Human Trajectory Following a Haptic Robotic Guide Using Recurrent Neural Networks

Social intelligence is an important requirement for enabling robots to collaborate with people. In particular, human path prediction is an essential capability for robots in that it prevents potential collision with a human and allows the robot to safely make larger movements. In this paper, we present a method for predicting the trajectory of a human who follows a haptic robotic guide without using sight, which is valuable for assistive robots that aid the visually impaired. We apply a deep learning method based on recurrent neural networks using multimodal data: (1) human trajectory, (2) movement of the robotic guide, (3) haptic input data measured from the physical interaction between the human and the robot, (4) human depth data. We collected actual human trajectory and multimodal response data through indoor experiments. Our model outperformed the baseline result while using only the robot data with the observed human trajectory, and it shows even better results when using additional haptic and depth data.Comment: 6 pages, Submitted to IEEE World Haptics Conference 201

STUDY OF FRICTION COMPENSATION MODEL FOR MOBILE ROBOT’S JOINTS

Frictional forces inside the joints of mobile robots hurt robot operation's stability and positioning accuracy. Therefore, establishing a suitable friction force compensation model has been a hot research topic in robotics. To explore the robot joint friction compensation model, three friction compensation models: linear, nonlinear, and neural network models, are developed in this paper. Based on the deep learning algorithm for three models at low speed, high speed, acceleration, and uniform speed training test, respectively results have been obtained. The test results show that the best friction compensation effect comes from combining neural network models in acceleration and a consistent speed state way. The friction compensation model trained this way yielded superior results to the other combinations tested. Finally, using the method, a friction compensation model trained by adding a neural network to the feedforward control torque was tested on a four-wheeled mobile robot platform. The test results show that the relative error of the torque caused by the friction of each joint is reduced by 15%-75% in 8 groups of tests, which indicates that our friction compensation method has a positive effect on improving the accuracy of the joint torque