Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a deep neural network to predict a measure that candidate motion data for an end effector of a robot will result in a successful grasp of one or more objects by the end effector. Some implementations are directed to utilization of the trained deep neural network to servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector. For example, the trained deep neural network may be utilized in the iterative updating of motion control commands for one or more actuators of a robot that control the pose of a grasping end effector of the robot, and to determine when to generate grasping control commands to effectuate an attempted grasp by the grasping end effector.

To prevent scattering of scattered objects such as of water, sand, mud, or snow accompanied by movement.

Scattering prevention constraint information is acquired based on road surface information. Movement is controlled based on the scattering prevention constraint information. For example, vibration information is detected by a vibration sensor or the like, and road surface information (information such as a road surface depth and a road surface type) is acquired based on the vibration information.

Provided is a robot control device capable of easily setting a robot operation speed which is safe for an operator. The robot control device is equipped with: a selection unit for selecting a location of a human body; an allowed speed storage unit for associating and storing the location of the human body and the allowed speed for the robot at said location; and a robot control unit for retrieving the allowed speed associated with the location selected by the selection unit from the allowed speed storage unit, and setting the smallest value for the retrieved allowed speed as the maximum speed for the robot.

Example wireless feedback control systems disclosed herein include a receiver to receive a first measurement of a target system via a first wireless link. Disclosed example systems also include a neural network to predict a value of a state of the target system at a future time relative to a prior time associated with the first measurement, the neural network to predict the value of the state of the target system based on the first measurement and a prior sequence of values of a control signal previously generated to control the target system during a time interval between the prior time and the future time, and the neural network to output the predicted value of the state of the target system to a controller. Disclosed example systems further include a transmitter to transmit a new value of the control signal to the target system via a second wireless link.

The present disclosure relates to protecting fragile members of robots from damage during fall events. In response to detecting a fall event, a fragile member of a robot can be actuated to a defensive configuration to avoid or reduce damage. An actuatable protective member can be actuated to protect a fragile member to avoid or reduce damage to the fragile member. Actuatable protective members can be dedicated protective members, or can be other members of the robot which serve different functionality outside of a fall event but act as a protective member during a fall event.

A robot control device includes: a creep-information storage unit that stores an amount of bending in correspondence with a cumulative time, the bending occurring in a robot due to creep deformation; a mastering-data storage unit that stores mastering data of the robot; a timer that measures the cumulative time; and a correction unit that corrects the mastering data stored in the mastering-data storage unit based on the amount of bending stored in the creep-information storage unit in correspondence with the cumulative time measured by the timer.

Disclosed herein is a robot including an output interface including at least one of a display or a speaker, a camera, and a processor controlling the output interface to output content, acquiring an image including a user through the camera while the content is output, detecting an over-immersion state of the user based on the acquired image, and controlling an operation of releasing over-immersion when the over-immersion state is detected.

A system for selection and configuration of an automated robotic process includes a media input module structured to receive at least one functional media, a media analysis module structured to analyze the at least one functional media and identify an action parameter; and a solution selection module structured to select at least one component of an AI solution for use in an automated robotic process, wherein the selection is based, at least in part, on the action parameter.

Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

A software compensated robotic system makes use of recurrent neural networks and image processing to control operation and/or movement of an end effector. Images are used to compensate for variations in the response of the robotic system to command signals. This compensation allows for the use of components having lower reproducibility, precision and/or accuracy that would otherwise be practical.