Intel Corporation

Siew Wen Chin

Sangeeta Ghangam Manepalli

Rita H Wouhaybi

Atul N Hatalkar

Hassnaa Moustafa

Glen J Anderson

Shao-Wen Yang

Rita Chattopadhyay

Chia-Hung Sophia Kuo

Ralf Graefe

Addicam V Sanjay

Rafael Rosales

Neslihan Kose Cihangir

Yang Peng

Fatema Adenwala

Ravindra V Narkhede

John Belstner

Haichuan Tan

Ruchika Singh

Hoang Tran Van

Disclosed herein are systems, devices, and methods of a robot safety system for improving the safety of human-robot collaborations. The robot safety system may include a processor that receives a collaboration policy for task collaboration between a robot and a collaborator. The task collaboration may include a movement plan of the robot to move a manipulator to a collaboration position. The robot safety system may also determine a new collaboration position based on the collaboration policy. The robot safety system may also update the collaboration position of the movement plan to the new collaboration position.

Repetitive task and contextual risk analytics for human-robot collaboration

Disclosed herein are embodiments of systems and methods for diversified imitation learning for automated machines. In an embodiment, a process-profiling system obtains sensor data captured by a plurality of sensors that are arranged to observe one or more human subjects performing one or more processes to accomplish one or more tasks. The process-profiling system clusters the sensor data based on a set of one or more process-performance criteria. The process-profiling system also performs, based on the clustered sensor data, one or both of generating and updating one or more process profiles in a plurality of process profiles. The process-profiling system selects, for one or more corresponding automated machines, one or more process profiles from among the plurality of process profiles, and the process-profiling system configures the one or more corresponding automated machines to operate according to the selected one or more process profiles.

Diversified imitation learning for automated machines

Methods, apparatus, systems, and articles of manufacture for user-to-avatar mapping and adjustment are disclosed. An example apparatus includes processor circuitry to at least one of instantiate or execute the machine readable instructions to: determine a dissonance between a first orientation of a user in a real-world environment and a second orientation of an avatar in a virtual environment, the avatar corresponding to the user; determine an avatar adjustment value based on the dissonance; and apply the avatar adjustment value to the avatar model to change the second orientation.

User-to-avatar action mapping and adjustment

A robot may include a processor, configured to operate according to a first operational mode or a second operational mode, wherein the first operational mode comprises performing an environmental perception operation using first sensor data, wherein the first sensor data comprise no camera data, and wherein the second operational mode comprises performing the environmental perception operation using second sensor data, wherein the second sensor data comprise camera data; while operating according to the first operational mode, determine a confidence factor representing a confidence of the environmental perception operation; determine whether or not the confidence factor is outside of a range; and if the confidence factor is outside of the range, operate according the second operational mode.

Robot sensor data management

Described herein is a technique that provides a first robot associated with a first trust domain to identify and authenticate a second robot associated with a second trust domain. The identification and authentication technique described herein is based on a robot processing a variety of input signals obtained via sensors, in order to generate robot identification information that can be compared with known or trusted information. Advantageously, the identification technique occurs locally, for example, at the robot and at the location of the robot-to-robot interaction, eliminating any requirement for communicating with an authoritative remote or cloud-based service, at the time and place of the robot-to-robot interaction.

Method to support autonomous robot-to-robot identification and task transfer

A robot including a resource sharing circuit, configured to operate according to a resource utilization limit, and comprising a processor, configured to receive resource data representing a resource utilization of the robot; if the resource utilization of the robot is within a predetermined range, operate according to a first operational mode, wherein an upper limit of the predetermined range is defined by a tolerance relative to the resource utilization limit; if the resource utilization of the robot is outside of the predetermined range, operate according to a second operational mode; wherein the first operational mode includes controlling a communication circuit to send a first wireless signal representing an availability to accept a task; and wherein the second operational mode includes controlling the communication circuit to send a second wireless signal representing an availability to allocate a task to an external device for remote processing.

Autonomous mobile robot scaling

Disclosed herein are systems and methods for controlling a telepresence robot, sometimes referred to as a receiver. The systems and methods may include obtaining environmental data associated with the receiver and/or an operator of the telepresence robot, sometimes referred to as a sender. A model defining a human intent may be received and an intent of a human proximate the receiver and or the sender may be determined using the model. A first signal may be transmitted to the receiver. The first signal may be operative to cause the receiver to alter a first behavior based on the intent of the human and/or the sender.

Multimodal intent recognition for telepresence robots

Localization data is accessed, which is based on information collected by one or more sensor devices deployed within an environment. The localization data identifies presence of a person within a particular location within the environment. A feature vector is access, which includes values to describe a plurality of different physical characteristics of the person as measured by a set of one or more sensor devices deployed within the environment when the person was present within the particular location. An emotion of the person is determined from the feature vector data, and the emotion is associated with the particular location within the environment. An emotion heat map is generated of the environment to map emotional responses determined within the environment to specific locations within the environment.

Emotion heat mapping

Techniques for monitoring the health of a device comprising a motor or generator are described. An apparatus to monitor the health of the device includes a plurality of sensors to detect root-mean-square (RMS) current values for a plurality of stator windings of the device. The apparatus also includes a processor to receive the RMS current values from the plurality of sensors and compute a fault indicator based on the RMS current values. The fault indicator represents a level of current imbalance between the plurality of stator windings due to winding faults.

Growing community of inventors

Penang, Malaysia

Siew Wen Chin