Honda Motor Co., Ltd.

Disney Enterprises, Inc.

Toyota Research Institute, Inc.

Umashankar Nagarajan

Ambarish Goswami

Gabriel Aguirre-Ollinger

Krishna Shankar

Kevin Stone

Jeremy C Ma

Max Bajracharya

Michael Laskey

Josh Petersen

Daniel Helmick

James Borders

Yoshiaki Sakagami

Katsu Yamane

Katsu Yumane

A method for performing a task by a robotic device includes mapping a group of task image pixel descriptors associated with a first group of pixels in a task image of a task environment to a group of teaching image pixel descriptors associated with a second group of pixels in a teaching image based on positioning the robotic device within the task environment. The method also includes determining a relative transform between the task image and the teaching image based on mapping the plurality of task image pixel descriptors. The relative transform indicates a change in one or more of points of 3D space between the task image and the teaching image. The method also includes performing the task associated with the set of parameterized behaviors based on updating one or more parameters of a set of parameterized behaviors associated with the teaching image based on determining the relative transform.

Virtual teach and repeat mobile manipulation system

A method for controlling a robotic device is presented. The method includes positioning the robotic device within a task environment. The method also includes mapping descriptors of a task image of a scene in the task environment to a teaching image of a teaching environment. The method further includes defining a relative transform between the task image and the teaching image based on the mapping. Furthermore, the method includes updating parameters of a set of parameterized behaviors based on the relative transform to perform a task corresponding to the teaching image.

The control method for lower-limb assistive exoskeletons assists human movement by producing a desired dynamic response on the human leg. Wearing the exoskeleton replaces the leg's natural admittance with the equivalent admittance of the coupled system formed by the leg and the exoskeleton. The control goal is to make the leg obey an admittance model defined by target values of natural frequency, resonant peak magnitude and zero-frequency response. The control achieves these objectives objective via positive feedback of the leg's angular position and angular acceleration. The method achieves simultaneous performance and robust stability through a constrained optimization that maximizes the system's gain margins while ensuring the desired location of its dominant poles.

Admittance shaping controller for exoskeleton assistance of the lower extremities

A resistive exoskeleton control system has a controller generating a positive resistance by shaping a closed loop integral admittance of a coupled human exoskeleton system wherein a frequency response magnitude of the integral admittance is lower than that of a natural human joint for desired frequencies of interest and generating an assistance ratio of approximately zero for the desired frequencies of interest.

Resistive exoskeleton control design framework

An assistive exoskeleton control system has a controller generating a positive assistance by shaping a closed loop integral admittance of a coupled human exoskeleton system to a desired assistance ratio Aby modifying a control transfer function using a cut-off frequency of a low pass filter.

Integral admittance shaping for an exoskeleton control design framework

The disclosure provides an approach for automatically determining task-specific robot model reductions. In one embodiment, a simplification application determines a smallest order statespace model whose stabilizing controller also stabilizes a full-order robot model. The simplification application may determine such a model via an iterative procedure in which the reduced order is initialized to the number of unstable poles of the open-loop full-order system and, while the closed loop full-order system with the balanced reduced order system's stabilizing controller is unstable, fractional balanced reduction is applied to generate a balanced reduced system. If one or more unstable closed-loop poles exist in the full-order system with the stabilizing controller of the newly-generated balanced reduced system, the reduced order is incremented by one, and fractional balanced reduction repeated, until no unstable closed-loop poles remain. In another embodiment, the model reduction is made task-specific by formulating the full model with task-specific outputs.

Automatic task-specific model reduction for humanoid robots

A robot, such as a bipedal robot, that includes three or more rigid links such as two legs and a pelvis. The robot includes joints pivotally connecting pairs of the rigid links and an actuator associated with each of the joints. The robot includes a universal balancing controller with an output feedback control module providing control signals to selectively drive the actuators to balance the robot on a support element which may be configured to provide a dynamic, unstable environment or to provide a static, stable environment. During use, the control signals are generated in response to processing of global robot data from sensors associated with the rigid links or the joints. The control signals are generated by the output feedback control module without any need for measurements of the support element or without any measurement of a dynamic environment.

Universal balancing controller for lateral stabilization of bipedal robots in dynamic unstable environments

A system and method is disclosed for controlling a robot having at least two legs, the robot falling down from an upright posture and the robot located near a plurality of surrounding objects. A plurality of predicted fall directions of the robot are determined, where each predicted fall direction is associated with a foot placement strategy, such as taking a step, for avoiding the surrounding objects. The degree to which each predicted fall direction avoids the surrounding objects is determined. A best strategy is selected from the various foot placement strategies based on the degree to which the associated fall direction avoids the surrounding objects. The robot is controlled to implement this best strategy.

Growing community of inventors

Sunnyvale, CA, United States of America

Umashankar Nagarajan