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.

One exemplary embodiment is a system comprising an operator input device structured to move in response to operator-applied force and to selectably output feedback force to the operator. A first computing system is structured to receive input from the operator input device and provide an output. A second computing system is structured to receive the output and provide a robot control command subject to a force constraint. An industrial robot system is in operative communication with the second computing system and comprises a robotic arm structured to move in response to the command. The second computing system is structured process the output to impose a force constraint using a dual threshold hysteresis control. The first computing system is structured to apply a feedback force to the operator input device correlated to force associated with the industrial robot system.

A robotic device includes a kinematic chain of a plurality of components, movable relative to each other; a sensor device configured to capture a force and/or moment exerted on at least one of the mobile components; a control device configured to control a movement of the at least one of the mobile components, in the direction of the force that is exerted, as a function of the force captured by the control device and/or of the moment captured by the control device; and a first capture device coupled to the control device and provided for the purpose of contactlessly capturing an operating action of an operator. In a normal operating mode, the control device is configured to specify a mobility of at least one of the mobile components as a function of the captured operating action, improving accuracy and reliability of the device in interaction with a human operator.

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 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.

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 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.

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 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.