Grippers for a picking device for drug packages and methods for operating the picking device are provided. The gripper has a tray having an end portion with an arcuate front, the end portion forming a loading and unloading front. The gripper further includes a transport device arranged above the tray and configured for moving drug packages from a storage surface onto the tray, and at least one sensor arranged in the end portion of the tray and configured to determine a presence of a drug package in a detection area of the at least one sensor.

Provided is a method, computer program product, and system for automatically assigning robotic devices to users based on need using predictive analytics. A processor may monitor activities performed by one or more users. The processor may determine, based on the monitoring, a set of activities that require assistance from a robotic device when being performed by the one or more users. The processor may match the set of activities to a set of capabilities related to a plurality of robotic devices. The processor may identify, based on the matching, a first robotic device that is capable of assisting the one or more users in performing a first activity of the set of activities. The processor may deploy the first robotic device to assist the one or more users in performing the first activity.

A telepresence robot may include a drive system, a control system, an imaging system, and a mapping module. The mapping module may access a plan view map of an area and tags associated with the area. In various embodiments, each tag may include tag coordinates and tag information, which may include a tag annotation. A tag identification system may identify tags within a predetermined range of the current position and the control system may execute an action based on an identified tag whose tag information comprises a telepresence robot action modifier. The telepresence robot may rotate an upper portion independent from a lower portion. A remote terminal may allow an operator to control the telepresence robot using any combination of control methods, including by selecting a destination in a live video feed, by selecting a destination on a plan view map, or by using a joystick or other peripheral device.

An autonomous companion mobile robot and system may complement the intelligence possessed by a user with machine learned intelligence to make a user's life more fulfilling. The robot and system includes a mobile robotic device and a mobile robotic docking station. Either or both of the mobile robotic device and the mobile robotic docking station may operate independently, as well as operating together as a team, as a system. The mobile robotic device may have an external form of a three-dimensional shape, a humanoid, a present or historical person, some fictional character, or some animal. The mobile robotic device and/or the mobile robotic docking station may each include a fog Internet of Things (IoT) gateway processor and a plurality of sensors and input/output devices. The autonomous companion mobile robot and system may collect data from and observe its users and offer suggestions, perform tasks, and present information to its users.

A robot-connected IoT-based sleep-caring system includes a sleep-caring robot and an IoT system. The sleep-caring robot includes environment monitoring, physiology monitoring, sleep monitoring, sound, lighting and electricity control, a smart storage compartment, central data processing, and machine arms. The IoT system senses and executes instructions from the sleep-caring robot, thereby catering to bedroom activities of the user.

A walking training robot according to the present disclosure includes: a main body part; a handle part disposed on the main body part for being griped by the user; a detecting part detecting a handle load applied to the handle part; a walking supporting part determining a load applied by the walking training robot to a walking exercise of the user based on the detected handle load; a moving device including a rotating body and controlling the rotating body to move the walking training robot based on the determined load of the walking training robot; a posture estimating part estimating a foot-lifting posture of the user based on the detected handle load; a training scenario generating part correcting a training scenario causing the user to perform a foot-lifting exercise, based on the foot-lifting posture; and a presenting part presenting an instruction to the user based on the training scenario.

Described herein are assistant robots that anticipate needs of one or more people (or animals). The assistant robots may recognize a current activity, knowledge of the person's routines, and contextual information. As such, the assistant robots can provide or offer to provide appropriate robotic assistance. The assistant robots can learn users' habits or be provided with knowledge regarding humans in its environment. The assistant robots develop a schedule and contextual understanding of the persons' behavior and needs. The assistant robots may interact, understand, and communicate with people before, during, or after providing assistance. The robot can combine gesture, clothing, emotional aspect, time, pose recognition, action recognition, and other observational data to understand people's medical condition, current activity, and future intended activities and intents.

Described herein are assistant robots that observe signs of core health, health dangers, and/or signs of medical distress in a home or at work. As such, the assistant robots can take actions to prevent dangerous situations, diagnose health problems, respond to requests for help, and provide regular treatments or analysis of a person's medical state. The assistant robots can learn users' habits or be provided with knowledge regarding humans in its environment. The assistant robots develop a schedule and contextual understanding of the persons' behavior and needs. The assistant robots may interact, understand, and communicate with people before, during, or after providing assistance. The robot can combine gesture, clothing, emotional aspect, time, pose recognition, action recognition, and other observational data to understand people's medical condition, current activity, and future intended activities and intents.

A robotic system includes retrieval of a priori stimulus based on an input that indicates a current health state and a target health state of a user. A set of test stimuli specific for the user is determined and a stimulus device is controlled to provide the set of test stimuli to the user. A first set of responses within the body of the user and a second set of responses discernible on the body of the user are determined, and a set of stimulus parameters for the stimulus device is calibrated based on the combination of the first set of responses, the second set of responses, the current health state, the target health state, and a trained AI-based system. A new stimulus is applied to a portion of the body of the user that shifts a condition of the user from the current health state towards the target health state.

A robotic system includes retrieval of a priori stimulus based on an input that indicates a current health state and a target health state of a user. A set of test stimuli specific for the user is determined and a stimulus device is controlled to provide the set of test stimuli to the user. A set of responses discernible on the body of the user is determined, and a set of stimulus parameters for the stimulus device is calibrated based on the combination of the set of responses, the current health state, the target health state, and a trained AI-based system. A new stimulus is applied to a portion of the body of the user that shifts a condition of the user from the current health state towards the target health state.