Brain Corporation

Qualcomm Technologies, Inc.

New York University

Arizona Board of Regents, a Body Corporate

Neurosciences Research Foundation, Inc.

Qualcomm Incorporated

Brian Corporation

Eugene Izhikevich

Jean-Baptiste Passot

Oleg Sinyavskiy

Filip Lukasz Piekniewski

Botond Szatmary

Patryk Laurent

Csaba Petre

Vadim Polonichko

Dimitry Fisher

Borja Ibarz Gabardos

Jayram Moorkanikara Nageswaran

Filip Ponulak

Micah Richert

Jeffrey Alexander Levin

Cody Griffin

Peter O'Connor

Marius Buibas

Philip Meier

Frank C Hoppensteadt

Sach Hansen Sokol

Heathcliff Hatcher

Victor Hokkiu Chan

John Black

Moslem Kazemi

Venkat Rangan

Jaldert Rombouts

Erik Christopher Malone

Mark Wildie

Varun Narendra

Michael-David Nakayoshi Canoy

Robert Howard Kimball

Jan Krzys Wegrzyn

Andrew Nolan Smith

Olivier Coenen

Oyvind Grotmol

Phil Duffy

Todd Hylton

Jim McCullough

Benjamin Neil Cipollini

Nicole Renke

Andrew Smith

Justin Couvignou

Jimmy Kim

Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.

Systems and methods for training a robot to autonomously travel a route

Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.

Adaptive predictor apparatus and methods

Systems and methods for a universal connection interface between a robot and a plurality of modular attachments are disclosed. The connection interface includes a data connection and a dynamic amplifier configured to adjust output of at least one electromechanically coupled mechanical output; and a processor configured to control gain of the dynamic amplifier.

Autonomous multi-tasking modular robotic system

Apparatus and methods for training and controlling of e.g., robotic devices. In one implementation, a robot may be utilized to perform a target task characterized by a target trajectory. The robot may be trained by a user using supervised learning. The user may interface to the robot, such as via a control apparatus configured to provide a teaching signal to the robot. The robot may comprise an adaptive controller comprising a neuron network, which may be configured to generate actuator control commands based on the user input and output of the learning process. During one or more learning trials, the controller may be trained to navigate a portion of the target trajectory. Individual trajectory portions may be trained during separate training trials. Some portions may be associated with robot executing complex actions and may require additional training trials and/or more dense training input compared to simpler trajectory actions.

Apparatus and methods for operating robotic devices using selective state space training

Apparatus and methods for training and controlling of, for instance, robotic devices. In one implementation, a robot may be trained by a user using supervised learning. The user may be unable to control all degrees of freedom of the robot simultaneously. The user may interface to the robot via a control apparatus configured to select and operate a subset of the robot's complement of actuators. The robot may comprise an adaptive controller comprising a neuron network. The adaptive controller may be configured to generate actuator control commands based on the user input and output of the learning process. Training of the adaptive controller may comprise partial set training. The user may train the adaptive controller to operate first actuator subset. Subsequent to learning to operate the first subset, the adaptive controller may be trained to operate another subset of degrees of freedom based on user input via the control apparatus.

Reduced degree of freedom robotic controller apparatus and methods

Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.

Predictive robotic controller apparatus and methods

Robotic devices may be trained by a user guiding the robot along a target trajectory using a correction signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. Training may comprise a plurality of trials. During an initial portion of a trial, the trainer may observe robot's operation and refrain from providing the training input to the robot. Upon observing a discrepancy between the target behavior and the actual behavior during the initial trial portion, the trainer may provide a teaching input (e.g., a correction signal) configured to affect robot's trajectory during subsequent trials. Upon completing a sufficient number of trials, the robot may be capable of navigating the trajectory in absence of the training input.

Apparatus and methods for online training of robots

Systems and methods for automatic detection of spills are disclosed. In some exemplary implementations, a robot can have a spill detector comprising at least one optical imaging device configured to capture at least one image of a scene containing a spill while the robot moves between locations. The robot can process the at least one image by segmentation. Once the spill has been identified, the robot can then generate an alert indicative at least in part of a recognition of the spill.

Systems and methods for automatic detection of spills

Systems and methods for robotic mobile platforms are disclosed. In one exemplary implementation, a system for enabling autonomous navigation of a mobile platform is disclosed. The system may include a memory having computer readable instructions stored thereon and at least one processor configured to execute the computer readable instructions. The execution of the computer readable instructions causes the system to: receive a first set of coordinates corresponding to a first location of a user; determine a different second location for the mobile platform; navigate the mobile platform between the second location and the first location; and receive a different second set of coordinates. Methods, apparatus and computer-readable mediums are also disclosed.

Systems and methods for robotic mobile platforms

Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.

Apparatus and methods for control of robot actions based on corrective user inputs

An optical object detection apparatus and associated methods. The apparatus may comprise a lens (e.g., fixed-focal length wide aperture lens) and an image sensor. The fixed focal length of the lens may correspond to a depth of field area in front of the lens. When an object enters the depth of field area (e.g., sue to a relative motion between the object and the lens) the object representation on the image sensor plane may be in-focus. Objects outside the depth of field area may be out of focus. In-focus representations of objects may be characterized by a greater contrast parameter compared to out of focus representations. One or more images provided by the detection apparatus may be analyzed in order to determine useful information (e.g., an image contrast parameter) of a given image. Based on the image contrast meeting one or more criteria, a detection indication may be produced.

Optical detection apparatus and methods

Robotic devices may be trained by a trainer guiding the robot along a target trajectory using physical contact with the robot. The robot may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. The trainer may observe task execution by the robot. Responsive to observing a discrepancy between the target behavior and the actual behavior, the trainer may provide a teaching input via a haptic action. The robot may execute the action based on a combination of the internal control signal produced by a learning process of the robot and the training input. The robot may infer the teaching input based on a comparison of a predicted state and actual state of the robot. The robot's learning process may be adjusted in accordance with the teaching input so as to reduce the discrepancy during a subsequent trial.

Apparatus and methods for haptic training of robots

Growing community of inventors

San Diego, CA, United States of America

Eugene Izhikevich