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.

Provided are systems and methods for training a robot. The method commences with collecting, by the robot, sensor data from a plurality of sensors of the robot. The sensor data may be related to a task being performed by the robot based on an artificial intelligence (AI) model. The method may further include determining, based on the sensor data and the AI model, that a probability of completing the task is below a threshold. The method may continue with sending a request for operator assistance to a remote computing device and receiving, in response to sending the request, teleoperation data from the remote computing device. The method may further include causing the robot to execute the task based on the teleoperation data. The method may continue with generating training data based on the sensor data and results of execution of the task for updating the AI model.

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.

A control device 1X mainly includes an operation sequence generation means 16X, a first robot control means 171X, a switching determination means 18X, and a second robot control means 172X. The operation sequence generation means 16X is configured to generate an operation sequence of a robot. The first robot control means 171X is configured to perform a first robot control that is a control of the robot based on the operation sequence. The switching determination means 18X is configured, during execution of the first robot control, to determine, based on the operation sequence, whether or not to switch to a second robot control, which is a control of the robot based on an external input. The second robot control means 172X is configured, if it is determined by the switching determination means 18X that the switching is required, to perform the second robot control.

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 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.

A robotic system includes a robot having a picking arm to grasp an inventory item and a shuttle. The shuttle includes a platform adapted to receive the inventory item from the picking arm of the robot. The platform is moveable between a pick-up location located substantially adjacent to the robot and an end location spaced a distance apart from the pick-up location. The system improves efficiency as transportation of the item from the pick-up location to the end location is divided between the robot and the shuttle.

Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

A robot system with a robot that has a camera and a remote control station that can connect to the robot. The connection can include a plurality of privileges. The system further includes a server that controls which privileges are provided to the remote control station. The privileges may include the ability to control the robot, joint in a multi-cast session and the reception of audio/video from the robot. The privileges can be established and edited through a manager control station. The server may contain a database that defines groups of remote control station that can be connected to groups of robots. The database can be edited to vary the stations and robots within a group. The system may also allow for connectivity between a remote control station at a user programmable time window.