Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that calibrate error aligned uncertainty for regression and continuous structured prediction tasks/optimizations. An example apparatus includes a prediction model, at least one memory, instructions, and processor circuitry to at least one of execute or instantiate the instructions to calculate a count of samples corresponding to an accuracy-certainty classification category, calculate a trainable uncertainty calibration loss value based on the calculated count, calculate a final differentiable loss value based on the trainable uncertainty calibration loss value, and calibrate the prediction model with the final differentiable loss value.

Abstract: Disclosed herein are systems, devices, and methods for an uncertainty-aware robot system that may perform a personalized risk analysis of an observed person. The uncertainty-aware robot system determines a recognized behavior for the observed person based on sensor information indicative of a gait of the observed person. The uncertainty-aware robot system determines an uncertainty score for the recognized behavior based on a comparison of the recognized behavior to potentially expected behaviors associated with the observed person and the environment. The uncertainty-aware robot system generates a remedial action instruction based on the uncertainty score.

Abstract: A computing device, including: a memory configured to store computer-readable instructions; and unintended human motion detection processing circuitry configured to execute the computer-readable instructions to cause the computing device to: interpret a human action; receive autonomous mobile robot (AMR) sensor data from an AMR sensor; and detect whether the human action is intended or unintended, wherein the detection is based on a predicted human action, a current human emotional or physical state, the interpreted human action, and the AMR sensor data.