US 12,420,401 B2
Multimodal end-to-end learning for continuous control of exoskeletons for versatile activities
Shuzhen Luo, Raleigh, NC (US); and Hao Su, Raleigh, NC (US)
Assigned to NORTH CAROLINA STATE UNIVERSITY, Raleigh, NC (US)
Filed by North Carolina State University, Raleigh, NC (US)
Filed on Oct. 2, 2023, as Appl. No. 18/375,797.
Claims priority of provisional application 63/411,754, filed on Sep. 30, 2022.
Prior Publication US 2024/0116170 A1, Apr. 11, 2024
Int. Cl. B25J 9/00 (2006.01); B25J 9/16 (2006.01)
CPC B25J 9/0006 (2013.01) [B25J 9/1615 (2013.01); B25J 9/163 (2013.01); B25J 9/1633 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method, comprising:
obtaining inertial measurement unit (IMU) sensor signals associated with an exoskeleton attached to a limb of a subject;
generating an exoskeleton control signal in response to the IMU sensor signals, the exoskeleton control signal generated by a control policy neural network trained offline from the exoskeleton using musculoskeletal human modeling and exoskeletal modeling with dynamics randomization; and
controlling joint torques of the exoskeleton exerted on the subject based upon the exoskeleton control signal.
 
10. An exoskeleton, comprising:
a support structure configured to interface with a limb of a user;
an inertial measurement unit (IMU) sensor configured to sense the limb of the user;
an actuator coupled to the support structure; and
processing circuitry configured to:
obtain IMU sensor signals associated with movement of the limb of the subject;
generate an exoskeleton control signal in response to the IMU sensor signals, the exoskeleton control signal generated by a control policy neural network trained offline from the exoskeleton using musculoskeletal human modeling and exoskeletal modeling with dynamics randomization; and
control joint torques of the exoskeleton exerted via the actuator based upon the exoskeleton control signal.