Abstract: The present disclosure relates to systems and methods for inducing adaptive learning in a biological neural network cultured in vitro. A multi-electrode array interfaces with the biological neural network and includes a plurality of recording electrodes and a plurality of stimulation electrodes. One or more processors characterize the neural network by delivering electrical stimulation to a plurality of putative neural units and measuring stimulus-evoked responses and select a neural configuration comprising at least one input neural unit, at least one output neural unit, and a plurality of training neural units. A simulated task is operated in closed loop with the biological neural network by encoding one or more task state variables as electrical stimulation delivered to the input neural unit, decoding control signals from activity of the output neural unit, and updating the simulated task.

Abstract: Methods and systems are provided for predictive shoulder kinematics via immersive virtual reality. In one example, a system comprises an immersive virtual reality (iVR) system, the iVR system including a headset and a hand-held controller, and machine readable instructions executable to: predict joint kinematics using a machine learning model based on motion data received from the iVR system during gameplay of a virtual reality-guided exercise with the iVR system. In this way, physical rehabilitation may be performed remotely with increased evaluation accuracy.

Abstract: A robotic arm having one or more hybrid (soft-rigid) joints includes a first link, a second link, and a joint interconnecting the first link and the second link, such that the first link is movable relative to the second link along an axis of motion. The joint includes: a socket component coupled to a distal end portion of the second link and a ball component coupled to a proximal end portion of first link, the ball component is configured to rotationally fit within the socket component. The joint also includes a flexible membrane encasing the socket component and the ball component. The robotic arm is controllable using a reinforcement learning algorithm training using a simulation of the robotic arm and optionally, further training in a physical world.

Abstract: A robotic arm having one or more hybrid (soft-rigid) joints includes a first link, a second link, and a joint interconnecting the first link and the second link, such that the first link is movable relative to the second link along an axis of motion. The joint includes: a socket component coupled to a distal end portion of the second link and a ball component coupled to a proximal end portion of first link, the ball component is configured to rotationally fit within the socket component. The joint also includes a flexible membrane encasing the socket component and the ball component. The robotic arm is controllable using a reinforcement learning algorithm training using a simulation of the robotic arm and optionally, further training in a physical world.