Millennial Net, Inc.

Empower Robotics Corporation

Sumitomo Heavy Industries, Ltd.

East Japan Railway Company

Boston Incubator Center, LLC

Manus Robotics, Inc.

Iii Holdings 1, LLC

Sheng Liu

Sokwoo Rhee

Haruhiko Harry Asada

Kosuke Takeuchi

Kota Weaver

Martin Hanssmann

John W Meincke, Ii

Baldin Adolfo Llorens-Bonilla

Kazumasa Shudo

Faye Y Wu

David Hirst

Tairin Hahn

Baldin Lloren-Bonilla

Mark D O'Hearne

Jun Maruyama

Lynne Thomas

Juyoung Kim

Methods and systems for determining changes in muscle activity patterns based on the optical response of subcutaneous human body structures are presented, along with methods and systems for controlling assistive robotic systems based on the measured muscle activity patterns. Elements of a muscle activity measurement system are mechanically coupled to a wearable structure that fits closely to a portion of the body of a human user. The muscle activity measurement system includes multiple emitters and detectors at different spacing along the skin surface. In some embodiments, the illumination intensity of each measurement channel, the programmable gain of each measurement channel, or both, are calibrated to maximize measurement sensitivity. In some embodiments, optical measurement data is employed to more accurately locate the muscle activity measurement system with respect to the human body. In some embodiments, a muscle activity measurement system tracks changes in muscle structure over time.

Methods and systems for muscle activity measurement and gesture detection

Wearable systems to partially support the weight of a human user engaged in task performance in a crouched position at or near ground level are presented herein. In one aspect, the wearable systems employ passive mechanisms and the configuration of the support mechanisms is changed by movements of the body of the human user while transitioning from a standing position to a crouched position, and vice-versa. In a further aspect, each passive lower body support assembly includes an auxiliary body support structure to enhance the support of the human user in a crouched position. In another further aspect, each auxiliary body support structure is deployed in coordination with the movement of the seat support structure that supports the human user. In some embodiments, one or more body support structures are constructed from an elastic material that conforms to the ground surface when loaded by the weight of the human user.

Lower body support system to facilitate floor level task execution by humans

Methods and system to partially support the weight of a human user engaged in task performance at ground level are presented herein. An upper body support system includes multiple degrees of freedom to adapt the physical geometry of the support system to meet the demands of the task at hand. Each degree of freedom of the upper body support system is passively controlled by the human user from a convenient user interface. In some embodiments, each end-effector of the support system is positioned and fixed in three degrees of freedom over a relatively large workspace by manually controlling the brake torque of two rotational joints and the state of a locking mechanism of a linear joint. In another aspect, an end-effector is removeably attached to the support system with a quick-change coupler. In some embodiments, a swivel joint mechanism compensates for misalignment with the work environment.

Control of multiple joints of an upper body support system

Methods and systems for navigating a mobile robot through a crowded pedestrian environment based on a trained navigation model are described herein. The trained navigation model receives measurement data identifying nearby pedestrians and the velocity of each nearby pedestrian relative to the mobile robot, and the current position and desired endpoint position of the mobile robot in the crowded pedestrian environment. Based on this information, the trained navigation model generates command signals that cause the mobile robot to adjust its velocity. By repeatedly sampling the velocities of surrounding pedestrians and current location, the navigation model directs the mobile robot toward the endpoint location with a minimum of disruption to the pedestrian traffic flows. The navigation model is trained to emulate desirable pedestrian walking behaviors in a crowded pedestrian environment by tracking the movement of a behavioral trainer through a crowded pedestrian environment.

Mobile robot that emulates pedestrian walking behavior

In some examples, a wireless controller is utilized within a wireless mesh network. The wireless controller includes a control module configured to manage at least one energy device via a pneumatic pressure line based on one or more parts of an energy profile; and a network interface module configured to transmit energy data to a management server and receive the one or more parts of the energy profile from the management server.

Systems and methods utilizing a wireless mesh network

A first device communicates with a wireless network that includes nodes that are active for predefined activation times and that are at least partially dormant when not active. The communication method includes identifying a start of communication via a message that exceeds a maximum activation time of nodes on the wireless network by a factor N, where N is equal to at least a maximum number of frequencies on the wireless network, and exchanging information with a second device comprising a node on the wireless network that is within a transmission range of the first device, where the information is exchanged following the message and includes frequency hopping data for the second device.

Communicating over a wireless network

A target device joins a wireless network by attempting to locate an existing routing device in a predefined neighborhood of the wireless network, designating the existing routing device as a master routing device if the existing routing device is located in the predefined neighborhood, where the master routing device is a primary recipient of data from the target device, and attempting to locate endpoint devices in the predefined neighborhood if the existing routing device is not located in the predefined neighborhood.

Protocol for configuring a wireless network

A multi-processor computing system included a plurality of processing units is provided in which each of the plurality of processing units operates at clock frequency and a coordinating protocol is used to assign tasks and operations to any of the plurality of processing units in a manner such that the power efficiency of the system is optimized.

Coordinating protocol for a multi-processor system

Growing community of inventors

Cambridge, MA, United States of America

Sheng Liu