Dynamic motion learning for multi-DOF flexible-joint robots using active–passive motor babbling through deep learning

<p>This paper proposes a learning strategy for robots with flexible joints having multi-degrees of freedom in order to achieve dynamic motion tasks. In spite of there being several potential benefits of flexible-joint robots such as exploitation of intrinsic dynamics and passive adaptation to environmental changes with mechanical compliance, controlling such robots is challenging because of increased complexity of their dynamics. To achieve dynamic movements, we introduce a two-phase learning framework of the <i>body dynamics</i> of the robot using a recurrent neural network motivated by a deep learning strategy. The proposed methodology comprises a <i>pre-training</i> phase with motor babbling and a <i>fine-tuning</i> phase with additional learning of the target tasks. In the pre-training phase, we consider <i>active</i> and <i>passive</i> exploratory motions for efficient acquisition of body dynamics. In the fine-tuning phase, the learned body dynamics are adjusted for specific tasks. We demonstrate the effectiveness of the proposed methodology in achieving dynamic tasks involving constrained movement requiring interactions with the environment on a simulated robot model and an actual PR2 robot both of which have a compliantly actuated seven degree-of-freedom arm. The results illustrate a reduction in the required number of training iterations for task learning and generalization capabilities for untrained situations.</p> <p>The proposed learning framework for acquiring body dynamics in two phases (pre-training and fine-tuning). In the pre-training phase, the robot acquires body dynamics with an RNN through motor babbling. We consider a sequence of active and passive motions to improve the efficiency in the learning process of the body dynamics. Then, in the fine-tuning phase, the robot performs additional learning to adjust acquired body dynamics to the target task. The objective of this strategy is to efficiently learn the desired movements to perform the given tasks with the reduction of training iterations and generalization to untrained situations with the learned body dynamics. The below listed points should be captured along with the Graphical abstract image Dynamic motion tasks for robots with flexible joints having multi-DOFs Pre-training with motor babbling and fine-tuning with additional learning Active and passive exploratory motions in motor babbling.</p

Functions of human olfactory mucus and age-dependent changes

Abstract Odorants are detected by olfactory sensory neurons, which are covered by olfactory mucus. Despite the existence of studies on olfactory mucus, its constituents, functions, and interindividual variability remain poorly understood. Here, we describe a human study that combined the collection of olfactory mucus and olfactory psychophysical tests. Our analyses revealed that olfactory mucus contains high concentrations of solutes, such as total proteins, inorganic elements, and molecules for xenobiotic metabolism. The high concentrations result in a capacity to capture or metabolize a specific repertoire of odorants. We provide evidence that odorant metabolism modifies our sense of smell. Finally, the amount of olfactory mucus decreases in an age-dependent manner. A follow-up experiment recapitulated the importance of the amount of mucus in the sensitive detection of odorants by their receptors. These findings provide a comprehensive picture of the molecular processes in olfactory mucus and propose a potential cause of olfactory decline