A Learning-Free Method for Locomotion Mode Prediction by Terrain Reconstruction and Visual-Inertial Odometry.

This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human movements and sparse features. Evidence of its effectiveness, both in terms of accuracy and operational time consumption, is substantiated through tests conducted using open-source dataset and closed-loop evaluations. Comprehensive experiments were undertaken to validate its prediction accuracy across various test conditions such as subjects, scenarios, sensor mounting positions, camera views, step sizes, walking directions, and disturbances from moving obstacles. A comprehensive prediction accuracy rate of 99.00% confirms the efficacy, generality, and robustness of the proposed method.

The Structural and Dynamical Properties of the Hydration of SNase Based on a Molecular Dynamics Simulation.

The dynamics of protein-water fluctuations are of biological significance. Molecular dynamics simulations were performed in order to explore the hydration dynamics of staphylococcal nuclease (SNase) at different temperatures and mutation levels. A dynamical transition in hydration water (at ~210 K) can trigger larger-amplitude fluctuations of protein. The protein-water hydrogen bonds lost about 40% in the total change from 150 K to 210 K, while the Mean Square Displacement increased by little. The protein was activated when the hydration water in local had a comparable trend in making hydrogen bonds with protein- and other waters. The mutations changed the local chemical properties and the hydration exhibited a biphasic distribution, with two time scales. Hydrogen bonding relaxation governed the local protein fluctuations on the picosecond time scale, with the fastest time (24.9 ps) at the hydrophobic site and slowest time (40.4 ps) in the charged environment. The protein dynamic was related to the water's translational diffusion via the relaxation of the protein-water's H-bonding. The structural and dynamical properties of protein-water at the molecular level are fundamental to the physiological and functional mechanisms of SNase.

A tale of two explanations: Enhancing human trust by explaining robot behavior.

The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicine bottles using (i) an embodied haptic prediction model to extract knowledge from sensory feedback, (ii) a stochastic grammar model induced to capture the compositional structure of a multistep task, and (iii) an improved Earley parsing algorithm to jointly leverage both the haptic and grammar models. The robot system not only shows the ability to learn from human demonstrators but also succeeds in opening new, unseen bottles. Using different forms of explanations generated by the robot system, we conducted a psychological experiment to examine what forms of explanations best foster human trust in the robot. We found that comprehensive and real-time visualizations of the robot's internal decisions were more effective in promoting human trust than explanations based on summary text descriptions. In addition, forms of explanation that are best suited to foster trust do not necessarily correspond to the model components contributing to the best task performance. This divergence shows a need for the robotics community to integrate model components to enhance both task execution and human trust in machines.