A method and system for robotic motion planning which perform dynamic velocity attenuation to avoid robot collision with static or dynamic objects. The technique maintains the planned robot tool path even when speed reduction is necessary, by providing feedback of a computed slowdown ratio to a tracking controller so that the path computation is always synchronized with current robot speed. The technique uses both robot-obstacle distance and relative velocity to determine when to apply velocity attenuation, and computes a joint speed limit vector based on a robot-obstacle distance, a maximum obstacle speed, and a computed stopping time as a function of the joint speed. Two different control structure implementations are disclosed, both of which provide feedback of the slowdown ratio to the motion planner as needed for faithful path following. A method of establishing velocity attenuation priority in multi-robot systems is also provided.

An apparatus state data structure for controlling a technical apparatus includes at least one capability data field and at least one associated data field. Each capability data field indicates a respective functionality of the technical apparatus. Each associated data field is associated with a respective capability data field. The at least one associated data field includes at least one required component state data field and at least one required diagnostic data field. Each required component state data field indicates a configuration of a respective component required for the functionality of the capability data field associated with the respective required component state data field. Each required diagnostic data field indicates a respective operational state of a component of the technical apparatus required for the functionality of the capability data field associated with the respective required diagnostic data field.

Systems and methods disclosed herein include a robotic arm positioned outside of the vehicle. The robotic arm may include an end effector configured as a cleaning implement for cleaning a surface in the interior of the vehicle. The system may include a first camera configured to determine a position of the vehicle with respect to a reference point. The system may include a second camera configured to scan the interior of the vehicle. The second system may include a first controller configured to create and/or modify a tool path to execute a cleaning operation, based on the scan, and to send instructions to the robotic arm to execute the cleaning operation in accordance with the created and/or modified tool path.

A machine for producing sanitary articles includes a plurality of processing stations including automated apparatus for forming sanitary articles which advance along a machine direction. The machine includes a multi-axis industrial robot arranged to automatically clean and/or inspect the automated apparatus of the processing stations. The machine has a positive impact on sustainability.

Devices, systems, and methods for social behavior of a telepresence robot are disclosed herein. A telepresence robot may include a drive system, a control system, an object detection system, and a social behaviors component. The drive system is configured to move the telepresence robot. The control system is configured to control the drive system to drive the telepresence robot around a work area. The object detection system is configured to detect a human in proximity to the telepresence robot. The social behaviors component is configured to provide instructions to the control system to cause the telepresence robot to operate according to a first set of rules when a presence of one or more humans is not detected and operate according to a second set of rules when the presence of one or more humans is detected.

System and method are disclosed for approximating unknown safety constraints during reinforcement learning of an autonomous agent. A controller for directing the autonomous agent includes a reinforcement learning (RL) algorithm configured to define a policy for behavior of the autonomous agent, and a control barrier function (CBF) algorithm configured to calculate a corrected policy that relocates policy states to an edge of a safety region. Iterations of the RL algorithm safely learn an optimal policy where exploration remains within the safety region. CBF algorithm uses standard least squares to derive estimates of coefficients for linear constraints of the safe region. This overcomes inaccurate estimation of safety region constraints caused by one or more noisy observations of constraints received by sensors.

A computing system may include a fail-safe learning engine configured to access camera data captured by multiple cameras positioned within an environment during a learning phase, generate training data based on the camera data captured by the multiple cameras, and construct a human detection model using the training data. The computing system may also include a fail-safe trigger engine configured to access camera data captured by the multiple cameras positioned within the environment during an active phase, and the camera data captured during the active phase may include a target object. The fail-trigger engine may further be configured to provide, as an input to the human detection model, the camera data that includes the target object and execute a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

Disclosed are a collision avoidance method for a moving device, a collision avoidance apparatus for a moving device, and a computer-readable storage medium. This application relates to the field of artificial intelligence technologies. According to the method, a parking direction of a moving device in an avoidance area is adjusted, so that a startup time used by the moving device after avoidance completes may be reduced. The method includes: determining a target path direction of a moving device; determining a first candidate parking direction and a second candidate parking direction; determining, based on the target path direction, a target parking direction of the moving device from the first candidate parking direction and the second candidate parking direction; and controlling, based on the target parking direction, the moving device to be parked in the avoidance area.

A detection system has an interface including a substrate supporting a conductive coating. Electrodes are provided to the substrate. A multiplexer provides current to the electrodes. A demultiplexer receives voltages from electrodes and provides corresponding signals to a controller. The controller receives these signals and determines therefrom an operation performed in connection with the interface by applying an algorithmic approach. Static interaction is recognizable, and machine learning can be used for gesture recognition and/or identification of other interaction types. The technology can be used in a broad array of applications, e.g., where it is desirable to sense interactions with a defined region such as, for example, in the case of touches, gestures, hovers, and/or the like.

Methods of collision handling for robotic surgical systems include slipping an input handle of a user interface of the robotic surgical system relative to a pose of a tool of a surgical robot of the robotic surgical system when a portion of the surgical robot collides with an obstruction and an input handle is moved in a direction that corresponds to moving the tool towards the obstruction. The input handle having an offset relative to a desired pose of the tool after the input handle is slipped.