Theory of Experimental Inference.

1949 ◽  
Vol 9 (4) ◽  
pp. 753
Author(s):  
Arthur Pap ◽  
C. West Churchman
Keyword(s):  
Experimental Inference

Theory of Experimental Inference. C. West Churchman

Isis ◽  
1949 ◽  
Vol 40 (1) ◽  
pp. 78-78
Author(s):  
V. F. Lenzen
Keyword(s):  
Experimental Inference

Theory of Experimental Inference.

1949 ◽  
Vol 44 (245) ◽  
pp. 136
Author(s):  
John W. Tukey ◽  
C. West Churchman
Keyword(s):  
Experimental Inference

Theory of Experimental Inference.

1950 ◽  
Vol 57 (1) ◽  
pp. 49 ◽  
Author(s):  
V. G. Hinshaw ◽  
C. W. Churchman
Keyword(s):  
Experimental Inference

scholarly journals Cognitive social zones for improving the pedestrian collision avoidance with mobile robots

2019 ◽  
Vol 42 (2) ◽  
pp. 07-14 ◽  
Author(s):  
Daniel Herrera ◽  
Javier Gimenez ◽  
Matias Monllor ◽  
Flavio Roberti ◽  
Ricardo Carelli
Keyword(s):  
Motion Control ◽  
Social Space ◽  
Learning Algorithm ◽  
Null Space ◽  
Social Cues ◽  
Potential Fields ◽  
Learning Stage ◽  
The Social ◽  
Holonomic Mobile Robot ◽  
Experimental Inference

Social behaviors are crucial to improve the acceptance of a robot in human-shared environments. One of themost important social cues is undoubtedly the social space. This human mechanism acts like a repulsive field to guaranteecomfortable interactions. Its modeling has been widely studied in social robotics, but its experimental inference has beenweakly mentioned. Thereby, this paper proposes a novel algorithm to infer the dimensions of an elliptical social zone froma points-cloud around the robot. The approach consists of identifying how the humans avoid a robot during navigationin shared scenarios, and later use this experience to represent humans obstacles like elliptical potential fields with thepreviously identified dimensions. Thus, the algorithm starts with a first-learning stage where the robot navigates withoutavoiding humans, i.e. the humans are in charge of avoiding the robots while developing their tasks. During this period,the robot generates a points-cloud with 2D laser measures from its own framework to define the human-presence zonesaround itself but prioritizing its closest surroundings. Later, the inferred social zone is incorporated to a null-space-based(NSB) control for a non-holonomic mobile robot, which consists of both trajectory tracking and pedestrian collisionavoidance. Finally, the performance of the learning algorithm and the motion control is verified through experimentation.

Relativism and Experimental Inference

1951 ◽  
Vol 18 (2) ◽  
pp. 155-169
Author(s):  
Frederick L. Will
Keyword(s):  
Experimental Inference
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