Abstract: Using various embodiments, an autonomous robot using data captured from a living subject are disclosed. In one embodiment, an autonomous robot can be trained using previously captured sensor data by receiving the sensor data, processing the sensor data and transmitting control signals to cause a movement in at least one portion of the autonomous robot. The movement can be related to an interaction of the autonomous robot with its surroundings, wherein the movement generates movement data. Thereafter, a predictive analysis can be performed on the movement data to learn a capability of generating actions that are spontaneous and adaptive to an immediate environment of the autonomous robot.

Abstract: Using various embodiments, an autonomous robot using data captured from a living subject are disclosed. In one embodiment, an autonomous robot can be trained using previously captured sensor data by receiving the sensor data, processing the sensor data and transmitting control signals to cause a movement in at least one portion of the autonomous robot. The movement can be related to an interaction of the autonomous robot with its surroundings, wherein the movement generates movement data. Thereafter, a predictive analysis can be performed on the movement data to learn a capability of generating actions that are spontaneous and adaptive to an immediate environment of the autonomous robot.

Abstract: Using various embodiments, methods, systems, and apparatuses are disclosed for data collection from a subject using a sensor apparatus. In one embodiment, an apparatus for use by a subject, the sensor apparatus is disclosed, comprising a sensor configured to capture and transmit data related to movements by the subject and a computing device coupled to the sensor to receive sensor data transmitted by the sensor, save the data in a track in a data file. The data file can have a plurality of tracks, each track saving data of a different sensor. The data thus saved can then be processed by another computing device.

Abstract: Using various embodiments, an autonomous robot using data captured from a living subject are disclosed. In one embodiment, an autonomous robot is described comprising a robotic skeleton designed similar to that of a human skeleton to simulate similar movements as performed by living subjects. The movements of the robotic skeleton are resultant due to control signals received by effectors present near or on the robotic skeleton. The robot can be configured to receive sensor data transmitted from a sensor apparatus that periodically gathers the sensor data from a living subject. The robot can then process the sensor data to transmit control signals to the effectors to simulate the actions performed by the living subject and perform a predictive analysis to learn the capability of generating spontaneous and adaptive actions, resulting in an autonomous robot that can adapt to its surroundings.

Abstract: Using various embodiments, methods, systems, and apparatuses are disclosed for data collection from a subject using a sensor apparatus. In one embodiment, an apparatus for use by a subject, the sensor apparatus is disclosed, comprising a sensor configured to capture and transmit data related to movements by the subject and a computing device coupled to the sensor to receive sensor data transmitted by the sensor, save the data in a track in a data file. The data file can have a plurality of tracks, each track saving data of a different sensor. The data thus saved can then be processed by another computing device.

Abstract: Using various embodiments, methods, systems, and apparatuses are disclosed for capturing the behavior of living subjects, where the data can be processed by a machine-learning algorithm to control an autonomous robot. In one embodiment, using the data captured by a user performing everyday operations, the autonomous robot can behave in a substantially similar manner as the living subject did in the environment where the behavior capture sessions of the living subject were carried out. Further, once the autonomous robot is provided with the processed data, the actions of the robot can iteratively be stored and processed by the machine learning algorithm to further refine the robot's movements performing the everyday operations. In another embodiment, behavior recorded from the living subject can be processed such that the autonomous robot is able to speak and understand spoken speech similar to that which occurred most frequently in the behavior capture sessions.

Abstract: Using various embodiments, an autonomous robot using data captured from a living subject are disclosed. In one embodiment, an autonomous robot is described comprising a robotic skeleton designed similar to that of a human skeleton to simulate similar movements as performed by living subjects. The movements of the robotic skeleton are resultant due to control signals received by effectors present near or on the robotic skeleton. The robot can be configured to receive sensor data transmitted from a sensor apparatus that periodically gathers the sensor data from a living subject. The robot can then process the sensor data to transmit control signals to the effectors to simulate the actions performed by the living subject and perform a predictive analysis to learn the capability of generating spontaneous and adaptive actions, resulting in an autonomous robot that can adapt to its surroundings.

Abstract: Using various embodiments, methods, systems, and apparatuses are disclosed for capturing the behavior of living subjects, where the data can be processed by a machine-learning algorithm to control an autonomous robot. In one embodiment, using the data captured by a user performing everyday operations, the autonomous robot can behave in a substantially similar manner as the living subject did in the environment where the behavior capture sessions of the living subject were carried out. Further, once the autonomous robot is provided with the processed data, the actions of the robot can iteratively be stored and processed by the machine learning algorithm to further refine the robot's movements performing the everyday operations. In another embodiment, behavior recorded from the living subject can be processed such that the autonomous robot is able to speak and understand spoken speech similar to that which occurred most frequently in the behavior capture sessions.