By Yasser Mohammad, Toyoaki Nishida
This publication explores an method of social robotics dependent completely on independent unsupervised innovations and positions it inside of a dependent exposition of comparable learn in psychology, neuroscience, HRI, and information mining. The authors current an self reliant and developmental process that permits the robotic to benefit interactive habit via imitating people utilizing algorithms from time-series research and computer studying.
The first half offers a entire and dependent advent to time-series research, switch aspect discovery, motif discovery and causality research targeting attainable applicability to HRI difficulties. particular motives of the entire algorithms concerned are supplied with open-source implementations in MATLAB allowing the reader to test with them. Imitation and simulation are the foremost applied sciences used to achieve social habit autonomously within the proposed procedure. half provides the reader a large review of analysis in those components in psychology, and ethology. according to this historical past, the authors speak about techniques to endow robots having the ability to autonomously how you can be social.
Data Mining for Social Robots should be crucial interpreting for graduate scholars and practitioners drawn to social and developmental robotics.
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Additional info for Data Mining for Social Robotics: Toward Autonomously Social Robots
Given this joint distribution, it is possible to generate time-series points by conditioning on time. This is the basic idea of Gaussian Mixture Regression (GMR) which is being widely applied in both statistics and learning from demonstration communities for providing a middle ground between high-bias parametric approaches and high-variance non-parametric approaches to modeling. The use of a GMM to model the joint distribution ensures that the conditional distribution is also a GMM. In such cases, Eq.
In: Demiris J, Birk A (eds) EWLR’98: the 7th european workshop on learning robots, pp 64–72 Nickel K, Stiefelhagen R (2007) Visual recognition of pointing gestures for human’ robot interaction. Image Vis Comput 25(12):1875–1884 Nicolescu M, Mataric M (2001) Learning and interacting in human-robot domains. IEEE Trans Syst Man Cybern Part A Syst Hum 31(5):419–430 Nicolescu M, Matari´c M (2002) A hierarchical architecture for behavior-based robots. In: AAMAS’02: the first international joint conference on autonomous agents and multiagent systems.
5 Examples of 2 dimensional time-series (represented by the two colors) generated from a Gaussian Markov Chain with μ = μ0 = 0 and unit Σ0 for four different Σ cases (Color in online) the MA(m) process with a = (1, 2, 3, 4, 5, 4, 3, 2, 1)T will tend to smooth out the time-series. 5 shows four time-series generated from this function. e. μ0 , Σ0 , and Σ). Given that μ0 and Σ0 affect only the first point of the time-series, we will focus on the effect of Σ. 5a shows an example 2D time-series when Σ = I which means that the two dimensions of the time-series are independent (because the off-diagonal elements are zeros) and they both have the same overall variance.