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.

A home robot device includes a memory, a movement module, and a processor. The processor is configured to execute a motion based on specified motion execution information stored in the memory, obtain feedback information of a user, generate modified motion execution information by modifying at least a portion of the specified motion execution information based on the feedback information of the user, where the modified motion execution information includes a movement value of at least one joint unit of the home robot device or at least one support linked to the at least one joint unit selected from a probability model of the specified motion execution information, and execute a motion of the home robot device based on the modified motion execution information.

A robot behavior generation method includes: generating a human body model; generating a robot model; creating associations between the human body model and the robot model; acquiring first motion data indicating movement of a human body model; selectively choosing movement feature indices having first unknown motion data that indicates movement of the human body model, and setting an evaluation standard for a reproduction error with respect to the normative motion data; selecting a constraint necessary for a robot to move, included in second unknown motion data that indicates the movement of the robot model; calculating first unknown motion data and second unknown motion data the reproduction errors of which based on the evaluation standard are minimum under the association and the constraint; and using the second unknown motion data to control the robot.

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.

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.

A humanoid robot which is capable of surveying its environment, notably to determine when humans are present and to engage in Activities with humans corresponding to an evaluation of their desires is provided. An operating system of the robot is configured in the robot to process the information received by Extractors (sensors and processing capabilities), to list Activities (gestures, dialogs, etc. . . . ) which are prioritized as a function of the current conditions and the history of engagement with the humans, to decide which Activity is to be launched and to have Actuators execute the Activity. Safeguard conditions of the robot are also taken into account in the list of Activities to be performed.

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.

A method and a system for controlling an apparatus using a behavior tree for an assigned task performed by the apparatus. The behavior tree includes a planner section and an activation section. The method includes calling the planner section, determining the state of the apparatus and setting an activation status by the planner section in response to being called, and evaluating the activation status before executing the assigned task.