Computing platforms, methods, and storage media for sensing and controlling with respect to a robot. A robot control and sensor system may include a pressure sensor configured to be mounted on a robot, and/or mounted on a robot peripheral, to measure a sensed pressure value at the robot. The pressure sensor operates with respect to first and second pressure thresholds. A controller is in communication with the pressure sensor and may be configured to: obtain, from the pressure sensor, a sensed pressure value relating to pressure applied to the robot at the pressure sensor; generate a soft reset notification to cause the robot to enter a soft reset mode when the sensed pressure value is above the first pressure threshold; and generate a hard reset notification to cause the robot to enter a hard reset mode when the sensed pressure value is above the second pressure threshold.

The present disclosure describes robots, tele-operation systems, methods, and computer program products where a robot is selectively operable in a plurality of control modes. Based on identification of a fault condition (when the robot fails to act in a suitable or sufficient manner), a control mode of the robot can be changed to provide a human operator with more explicit control over the robot. In this way, the fault condition can be resolved by human operator input, and the control modes, AI, or control paradigm for the robot can be trained to perform better in the future.

The present disclosure describes robots, tele-operation systems, methods, and computer program products where a robot is selectively operable in a plurality of control modes. Based on identification of a fault condition (when the robot fails to act in a suitable or sufficient manner), a control mode of the robot can be changed to provide a human operator with more explicit control over the robot. In this way, the fault condition can be resolved by human operator input, and the control modes, AI, or control paradigm for the robot can be trained to perform better in the future.

A motion dependency surgical robotic system (100) employs an independent robotic arm (20), a dependent robotic arm (21), and a motion dependency robot controller (104). In operation, the motion dependency robot controller (104) controls an independent motion of the independent robotic arm (20) within a coordinate space responsive to an input signal indicative of the motion of the independent robotic arm (20) within the coordinate space, and further controls a motion of the dependent robotic arm (21) within the coordinate space as a function of a spatial geometric relationship between the independent robotic arm (20) and the dependent robotic arm (21) within the coordinate space.

A robot agent (102) includes an electro-mechanical subsystem (202), a sensor subsystem (204) having one or more sensors, and a computer hardware subsystem (206) to execute one or more sets of executable instructions (212, 214, 216, 218, 220). The one or more sets of executable instructions manipulate the robot agent to predict an action to be implemented by the robot agent in performing a task (112) and predict whether the robot agent will fail in performing the action. The one or more sets of executable instructions further manipulate the robot agent to, responsive to predicting the robot agent will fail in performing the action, obtain guidance input (116) for the first action from at least one guidance source, the guidance input representing guidance for performing the action by the robot agent, and manipulate the electro-mechanical subsystem to perform the action using the guidance input.

This disclosure relates generally to navigation of a tele-robot in dynamic environment using in-situ intelligence. Tele-robotics is the area of robotics concerned with the control of robots (tele-robots) in a remote environment from a distance. In reality the remote environment where the tele robot navigates may be dynamic in nature with unpredictable movements, making the navigation extremely challenging. The disclosure proposes an in-situ intelligent navigation of a tele-robot in a dynamic environment. The disclosed in-situ intelligence enables the tele-robot to understand the dynamic environment by identification and estimation of future location of objects based on a generating/training a motion model. Further the disclosed techniques also enable communication between a master and the tele-robot (whenever necessary) based on an application layer communication semantic.

A method for adjusting a system autonomy level of a cargo handling system configured for autonomous control by a processor is disclosed. In various embodiments, the method includes receiving by the processor a sensor database from a plurality of sensing agents in operable communication with the processor; determining by the processor a confidence level based on the sensor database; and adjusting by the processor the system autonomy level for continued operation of the cargo handling system.

The system, devices and methods disclosed herein enable autonomous and tele-operation of tele-operated robots for maintenance of a property around known and unknown obstacles. A method may include using an unmanned aerial vehicle for obtaining additional data relating to the property and obstacles within the property and plan a path around the obstacles using data from sensors on-board the tele-operated robot and the aerial image. A method may also provide optimization of total time needed for performing the property maintenance and the labor costs in situations where manual intervention is needed for navigating the tele-operated robot around obstacles on the property or for removing obstacles on the property.

A method to enable autonomous and tele-operation of tele-operated robots for maintenance of a property around known and unknown obstacles may include using an unmanned aerial vehicle for obtaining additional data relating to the property and obstacles within the property and plan a path around the obstacles using data from sensors on-board the tele-operated robot and the aerial image. A method may also provide optimization of total time needed for performing the property maintenance and the labor costs in situations where manual intervention is needed for navigating the tele-operated robot around obstacles on the property or for removing obstacles on the property. Embodiments further include systems and devices practicing the method.

An autonomous robot with on demand human intervention is disclosed. In various embodiments, a robot operates in an autonomous mode of operation in which the robot performs one or more tasks autonomously without human intervention. The robot determines that a strategy is not available to perform a next task autonomously. In response to the determination, the robot enters a human intervention mode of operation.