Samsung Electronics Co., Ltd.

Energium Co., Ltd.

Ginus Co., Ltd.

Kang-Hee Lee

Kwang-Choon Kim

Jong-Hwan Kim

Se-Hyoung Cho

Ye-Hoon Kim

Seung-Hwan Cho

Seung-Hwan Choi

Sung-Lim Kim

Jung-Hun Kang

Jin-Sam Ju

Chang-Soon Park

Chan-Ho Pak

Kyung-Jung Kwon

A genetic robot platform and a genetic robot behavior expression method. When a controller of a robot platform driving a genetic robot, which has robot-behavior-expression-related information as gene information and of which an expression behavior is determined according to the gene information, recognizes a stimulation, the controller determines whether there is an instinctive behavior corresponding to the recognized stimulation. When there is an instinctive behavior corresponding to the recognized stimulation, the controller determines whether the recognized stimulation corresponds to the first generated stimulation or a repeatedly-generated stimulation, and determines an instinctive behavior to be expressed according to the recognized stimulation and the number of times of repeated stimulations. Then, the controller enables the robot to perform the instinctive behavior corresponding to the currently-recognized stimulation by expressing the determined instinctive behavior. Accordingly, there is an effect of providing a robot which is implemented to be more similar to real life.

Genetic robot platform and genetic robot behavior expression method

A labeling and preparation device for test tubes. At least one storage section places at least one pallet having a set of test tubes at a preset position. At least one transfer section is disposed above the storage section, and clamps or releases one of the test tubes. A labeling section is disposed below the transfer section, receives the test tubes transferred from the transfer section, and attaches a label having information related to a sample on a circumference of each test tube. An ejection section is disposed below the labeling section, and ejects the test tubes to an outside. A controller controls drivers of the transfer section, the labeling section and the ejection section.

Labeling and preparation device for test tubes

Disclosed is a software robot apparatus and a method including detecting environmental information, detecting multiple external events objects, generating a sensor value having an effect on the software robot, changing physical states related to external events and internal events, generating a physical state value, changing a perception state, generating a perception state value corresponding to the changed perception state, changing an emotion state, generating an emotion state value; detecting an episode related to a behavior type, storing a variance related to each state, calculating a representative variance, and when a current perception state or a current emotion state is identified as unstable, detecting an episode capable of changing the unstable state into a normal state, determining a behavior and an object stored in the detected episode as a final behavior object, and expressing an actual behavior of the software robot to the object corresponding to the final behavior object.

Apparatus and method for expressing behavior of software robot

Disclosed is a method and apparatus for learning behavior in a software robot. The method includes detecting a kind of an object in cyberspace related to a kind of presently manifested action, and a kind and the variation of at least one state among percept states or emotional states preset so as to change in relation to the kind of the action; finding episodes respectively corresponding to each of one or more objects in the cyberspace, each of one or more emotional states and each of one or more percept states, respectively defined in the software robot, a kind of an object in cyberspace related to the detected kind of the action among multiple episodes for responding a combination of kinds of respective one or more actions and for storing variation related to each state, and a kind of at least one state among percept states or emotional states preset so as to change in relation to the kind of the action; using variation stored in response to the found episode and variation generated in response to the manifested action, and calculating a representative variation; and storing the representative variation as a variation of the found episode.

Method and apparatus for learning behavior in software robot

A system and method for generating a robot genome. Internal state attribute preferences of internal states and behavior preferences are input by a user, and a set of one or more robot genomes containing initial values of genes is generated. A masking process of each robot genome of the generated robot genome group is performed so attributes are manifested. Robot genomes that underwent the masking process are loaded into a personality engine. A reliability value is calculated from a manifestation result of each robot genome, and reliability values of extracted robot genomes that satisfy a particular user reliability value are determined. Extracted robot genomes are modified when the user reliability value is not satisfied, and a new robot genome group including the modified robot genomes is generated. A robot genome satisfying the user reliability is extracted by iteratively performing the above procedures for the newly generated robot genome group.

System and method for generating robot genome

Disclosed is a method for changing emotion of a software robot (sobot) in a sobot terminal. The sobot terminal includes a short-term memory unit and a long-term memory unit. When an emotional change occurs during the operation of the short-term memory unit, the sobot terminal stores a short-term reference emotion value of the corresponding emotion in the long-term reference memory. When the operation time of the long-term memory unit elapses, the sobot terminal resets the long-term reference emotion value using the short-term reference emotion values stored in the long-term memory unit. The sobot terminal can change the emotion of the sobot more naturally through the above process.

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Seoul, South Korea

Kang-Hee Lee