Embodiments of a method and system for sharing toy robot programs enabling toy robots to interact with physical surroundings can include receiving a robot program; automatically processing a token for the robot program; and processing a program request for the robot program based on the token. The embodiments can additionally or alternatively include controlling a toy robot based on a robot program; recommending a robot program; publishing a robot program; processing modifications of robot programs S160; and/or any other suitable functionality.

A method is provided for operating a robotic device having a kinematic chain of movable components. The method includes: detecting respective values at least one characteristic of a plurality of the movable components by sensors arranged on the kinematic chain or in the vicinity of the kinematic chain; ascertaining a maximum value based on the detected values; comparing the ascertained maximum value with a predefined first safety limit by a controller of the robotic device; and adjusting the at least one characteristic or a further characteristic of the kinematic chain when the ascertained maximum value has a predefined relationship with the first safety limit, in order to increase the operating safety of the robotic device.

Apparatus and methods for a modular robotic device with artificial intelligence that is receptive to training controls. In one implementation, modular robotic device architecture may be used to provide all or most high cost components in an autonomy module that is separate from the robotic body. The autonomy module may comprise controller, power, actuators that may be connected to controllable elements of the robotic body. The controller may position limbs of the toy in a target position. A user may utilize haptic training approach in order to enable the robotic toy to perform target action(s). Modular configuration of the disclosure enables users to replace one toy body (e.g., the bear) with another (e.g., a giraffe) while using hardware provided by the autonomy module. Modular architecture may enable users to purchase a single AM for use with multiple robotic bodies, thereby reducing the overall cost of ownership.

Embodiments of a method and system for sharing toy robot programs enabling toy robots to interact with physical surroundings can include receiving a robot program; automatically processing a token for the robot program; and processing a program request for the robot program based on the token. The embodiments can additionally or alternatively include controlling a toy robot based on a robot program; recommending a robot program; publishing a robot program; processing modifications of robot programs S160; and/or any other suitable functionality.

Generating a robot control policy that regulates both motion control and interaction with an environment and/or includes a learned potential function and/or dissipative field. Some implementations relate to resampling temporally distributed data points to generate spatially distributed data points, and generating the control policy using the spatially distributed data points. Some implementations additionally or alternatively relate to automatically determining a potential gradient for data points, and generating the control policy using the automatically determined potential gradient. Some implementations additionally or alternatively relate to determining and assigning a prior weight to each of the data points of multiple groups, and generating the control policy using the weights. Some implementations additionally or alternatively relate to defining and using non-uniform smoothness parameters at each data point, defining and using d parameters for stiffness and/or damping at each data point, and/or obviating the need to utilize virtual data points in generating the control policy.

A method for programmatic robot control including, at a user device: displaying a set of icons and receiving a program for a robot, wherein receiving a program for a robot includes receiving a selection of a state icon associated with a state; associating a transition with the state; and creating a relationship between the transition and an event. The method functions to provide a user with an environment for creating a program to control the robot, and can additionally function to control the robot.

Generating a robot control policy that regulates both motion control and interaction with an environment and/or includes a learned potential function and/or dissipative field. Some implementations relate to resampling temporally distributed data points to generate spatially distributed data points, and generating the control policy using the spatially distributed data points. Some implementations additionally or alternatively relate to automatically determining a potential gradient for data points, and generating the control policy using the automatically determined potential gradient. Some implementations additionally or alternatively relate to determining and assigning a prior weight to each of the data points of multiple groups, and generating the control policy using the weights. Some implementations additionally or alternatively relate to defining and using non-uniform smoothness parameters at each data point, defining and using d parameters for stiffness and/or damping at each data point, and/or obviating the need to utilize virtual data points in generating the control policy.

An object of the present invention is to sense forces from a fine external force to a large external force with high precision, and enable high-precision control. A multi-joint robot arm has a first sensor arranged in an end portion, and second sensors arranged in joints, respectively. The first sensor and the second sensor have different detectable ranges from each other for a detectable force. A controlling apparatus selects which sensing result of a sensor should be used between sensing results of the first sensor 131 and the second sensor, and controls a robot arm by using the selected sensing result of the sensor.

A method for programmatic robot control including, at a user device: displaying a set of icons and receiving a program for a robot, wherein receiving a program for a robot includes receiving a selection of a state icon associated with a state; associating a transition with the state; and creating a relationship between the transition and an event. The method functions to provide a user with an environment for creating a program to control the robot, and can additionally function to control the robot.

A teaching method for an industrial robot may include, while the hand is lifted up and down by the lifting mechanism, detecting, with the first sensor, a position of either the installation unit or a teaching jig to be installed on the installation unit in a vertical direction; while the hand is transferred in the first direction by the transfer mechanism, detecting, with the first sensor, a position of either the installation unit or the teaching jig in the first direction; while the hand is transferred in the first direction by the transfer mechanism, detecting, with the second sensor, a position of either the installation unit or the teaching jig in the first direction; and calculating a position of either the installation unit or the teaching jig in the second direction, in order to teach a position of the transfer object to be installed on the installation unit.