Sanctuary Cognitive Systems Corporation

Giant Ai, Inc.

Adrian L Kaehler

Jeff Kranski

Carolyn Wales

Chris Cianci

Kyle J Martin

Christopher M Cianci

Jeffrey S Kranski

Provided is a process, including: obtaining, with a computer system, access to a specification indicating which regions of an embedding space are designated as anomalous relative to vectors in the embedding space characterizing past behavior of a first instance of a dynamical system; receiving, with the computer system, multi-channel input indicative of a state of a second instance of the dynamical system; and classifying, with the computer system, whether the state of the second instance of the dynamical system is anomalous by: encoding the multi-channel input into a vector in the embedding space; causing the specification to be applied to the vector; obtaining a result of applying the specification to the vector; and classifying whether the state of the second instance of the dynamical system is anomalous based on the result; and storing the classification in memory.

Anomaly detection in latent space representations of robot movements

A robot system includes a first computing device and a second computing device. The first computing device is configured to control operation of the robot based on data flows received from a plurality of sensors of the robot and the second computing system is configured to receive and process at least some of the data flows concurrently while the first computing system controls operation of the robot.

System for presenting robotic data flows for application development

Provided is a process that includes obtaining data indicative of state of a dynamic mechanical system and an environment of the dynamic mechanical system, the data comprising a plurality of channels of data from a plurality of different sensors including a plurality of cameras and other sensors indicative of state of actuators of the dynamic mechanical system; forming a training set from the obtained data by segmenting the data by time and grouping segments from the different channels by time to form units of training data that span different channels among the plurality of channels; training a metric learning model to encode inputs corresponding to the plurality of channels as vectors in an embedding space with self-supervised learning based on the training set; and using the trained metric learning model to control the dynamic mechanical system or another dynamic mechanical system.

Spatio-temporal consistency embeddings from multiple observed modalities

A robot is provided having a kinematic chain comprising a plurality of joints and links, including a root joint connected to a robot pedestal, and at least one end effector. A plurality of actuators are fixedly mounted on the robot pedestal. A plurality of tendons is connected to a corresponding plurality of actuation points on the kinematic chain and to actuators in the plurality of actuators, arranged to translate actuator position and force to actuation points for tendon-driven joints on the kinematic chain with losses in precision due to variability of tendons in the plurality of tendons. A controller operates the kinematic chain to perform a task. The controller is configured to generate actuator command data in dependence on the actuator states and image data in a manner that compensates for the losses in precision in the tendon-driven mechanisms.

Artificial intelligence-actuated robot

Disclosed techniques for decreasing teach times of robot systems may obtain a first set of parameters of a first trained robot-control model of a first robot trained to perform a task and determine, based on the first set of parameters, a second set of parameters of a second robot-control model of a second robot before the second robot is trained to perform the task. In some cases, a plurality of sets of parameters from trained robot-control models of respective robots trained to perform a task may be obtained. Thus, for example, a convergence of values of those parameters on a value, or range of potential values, may be determined. Embodiments may determine values for parameters of the control model of the (e.g., second) robot to be trained within a range, or a threshold, based on values of corresponding parameters of the trained robot(s).

Expedited robot teach-through initialization from previously trained system

A system includes a robot having contact surfaces and a sensor array having a spatially distributed touch sensors disposed on the contact surfaces. Each touch sensor has an identifier and outputs an analog signal at a set of frequencies associated with the identifier The sensor array outputs a combined analog signal representative of a combination of the analog signals outputted by the touch sensors. The system includes an analog-to-digital converter that generates a digital signal in a time domain based on the combined analog signal. The system includes one or more processing units that transforms the digital signal from the time domain to a frequency domain and detects locations of touch within the sensor array based on frequencies observed the frequency domain and the identifiers of the touch sensors.

Combined analog and digital architecture for handling sensory input data

Provided is a process, including: obtaining, with a computer system, a set of tasks to be performed by a fleet of robots; obtaining, with the computer system, for each task in the set of tasks, a respective plurality of duty cycles, each corresponding to an amount of usage of a respective actuator of a robot among the fleet of robots upon performing the respective task; accessing, with the computer system, for each robot in the fleet of robots, a current wear-state vector having dimensions corresponding to cumulative wear on actuators of the respective robots; and based on the current wear-state vectors and the duty cycles of the tasks, with the computer system, assigning the tasks to the robots in the fleet of robots.

Scheduler for robot fleets

Provided is a robot that includes: a first sensor having a first output and configured to sense state of a robot or an environment of the robot; a first hardware machine-learning accelerator coupled to the first output of the first sensor and configured to transform information sensed by the first sensor into a first latent-space representation; a second sensor having a second output and configured to sense state of the robot or the environment of the robot; a second hardware machine-learning accelerator configured to transform information sensed by the second sensor into a second latent-space representation; and a processor configured to control the robot based on both the first latent-space representation and the second latent-space representation.

Hybrid computing achitectures with specialized processors to encode/decode latent representations for controlling dynamic mechanical systems

A software compensated robotic system makes use of recurrent neural networks and image processing to control operation and/or movement of an end effector. Images are used to compensate for variations in the response of the robotic system to command signals. This compensation allows for the use of components having lower reproducibility, precision and/or accuracy that would otherwise be practical.

Software compensated robotics

Provided is a system that includes: a plurality of touch sensors sharing a signal medium, each touch sensor in the plurality being configured to output set of frequencies on the signal medium responsive to being touched, each touch sensor in the plurality being configured to output a different set of frequencies; an analog to digital converter electrically coupled to the signal medium and configured to receive the sets of frequencies from the touch sensors and convert the sets of frequencies to digital representations of the sets of frequencies in the time domain; a processor communicatively coupled to the analog to digital converter and configured to execute a fast Fourier transform of the digital representations from the time domain into digital representations in the frequency domain; and an address decoder operative to transform the digital representations in the frequency domain into identifiers of touch sensors among the plurality of touch sensors.

Growing community of inventors

Boulder Creek, CA, United States of America

Adrian L Kaehler