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.

A method for toy robot programming, the toy robot including a set of sensors, the method including, at a user device remote from the toy robot: receiving sensor measurements from the toy robot during physical robot manipulation; in response to detecting a programming trigger event, automatically converting the sensor measurements into a series of puppeted programming inputs; and displaying graphical representations of the set of puppeted programming inputs on a programming interface application on the user device.

An industrial robot having high operability for a user is provided. An industrial robot includes a manipulator, a controller which controls an operation of the manipulator, and a detection device attached to the manipulator and detecting a gesture input. The controller executes a process corresponding to the detected gesture input.

Methods and systems for enabling human-machine collaborations include a generalizable framework that supports dynamic adaptation and reuse of robotic capability representations and human-machine collaborative behaviors. Specifically, a method of feedback-enabled user-robot collaboration includes obtaining a robot capability that models a robot's functionality for performing task actions, specializing the robot capability with an information kernel that encapsulates task-related parameters associated with the task actions, and providing an instance of the specialized robot capability as a robot capability element that controls the robot's functionality based on the task-related parameters. The method also includes obtaining, based on the robot capability element's user interaction requirements, user interaction capability elements, via which the robot capability element receives user input and provides user feedback, controlling, based on the task-related parameters, the robot's functionality to perform the task actions in collaboration with the user input; and providing the user feedback including task-related information generated by the robot capability element in association with the task actions.

Provided is a robot system including a robot; a control device configured to control the robot; a portable teach pendant connected to the control device; and a teaching handle attached to the robot and connected to the control device, where the teach pendant is provided with a first enable switch configured to permit operation of the robot by the teach pendant, the teaching handle is provided with a second enable switch configured to permit operation of the robot by the teaching handle, and the control device enables operation of the robot by the teaching handle only when the first enable switch is in an off state and the second enable switch is switched to the on state, and enables operation of the robot by the teach pendant only when the second enable switch is in an off state and the first enable switch is switched to the on state.

A control device includes a processor that is configured to be capable of displaying, on a display, a first setting form capable of setting a plurality of first setting items related to force control performed using an output of a force sensor included in a robot and a second setting form capable of setting, as one second setting item, at least a pair of the first setting items among the plurality of first setting items included in the first setting form.

A method for operating a robotic device with a kinematic chain of mobile components is provided. The kinematic chain includes a function-specific end-effector at one end. Sensor values are acquired by sensors of the robotic device arranged on the kinematic chain or in the environment of the kinematic chain. A force acting on the end-effector or another component of the kinematic chain, or a variable dependent thereupon, is determined in a prespecified manner based on the acquired sensor values. The force or variable determined is compared with a prespecified first safety limit value by a control mechanism of the robotic device. A characteristic of the kinematic chain or the function-specific end-effector is adapted if the force or variable determined is in a prespecified relationship to the first safety limit value in order to increase the operational safety of a robotic device and of people in the environment of the robotic device.

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 robot system includes a robot, a vision sensor, and a controller. The vision sensor is configured to be detachably attached to the robot. The controller is configured to measure a reference object by using the vision sensor and calibrate a relative relationship between a sensor portion of the vision sensor and an engagement portion of the vision sensor, and teach the robot by referring to the relative relationship and by using the vision sensor, after the vision sensor is attached to the robot.

A method for automatically ascertaining an input command for a robot, wherein the input command is entered by manually exerting an external force onto the robot. The input command is ascertained on the basis of the joint force component attempting to cause a movement of the robot in only one robot joint coordinate sub-space which is specific to the input command. The joint forces are imprinted with the external force.