Structural Transformations in Ferroelectrics Discovered by Raman Spectroscopy

Ferroelectrics systems are of great interest from the fundamental as well as applications points, such as ferroelectric random access memories, electro-optic switches and a number of electro-optic devices. Curie temperature (TC) is one of the important parameters of ferroelectrics for high-temperature applications. Particularly, the optical modes, which are associated with the ferroelectric to paraelectric phase transition, are of great interest. Structural transformations that alter the crystal symmetry often have a significant effect on the Raman spectroscopy. This chapter systematically studies the type ferroelectric oxides and rare earth element doped ferroelectric materials such as PbTiO3-Bi(Mg0.5Ti0.5)O3 (PT-BMT), Sr x Ba1−x Nb2O6 (SBN), Pb1−1.5x La x Zr0.42Sn0.4Ti0.18O3 (PLZST), Bi1−xLaxFe1−yTiyO3 (BLFT) and (K0.5Na0.5)NbO3-0.05LiNbO3 (KNN-LN) and so on synthesis of single crystal/ceramic and optical phonon vibration modes and the improvement of the Curie temperature characteristic using spectrometry measurements. The TC, distortion degree, and phase structure of the ferroelectric materials have been investigated by temperature-dependent Raman spectroscopy. Meanwhile, the important physical parameters exhibited a strong dependence on dopants resulting in structural modifications and performance promotion

Learn to Grasp via Intention Discovery and its Application to Challenging Clutter

Humans excel in grasping objects through diverse and robust policies, many of which are so probabilistically rare that exploration-based learning methods hardly observe and learn. Inspired by the human learning process, we propose a method to extract and exploit latent intents from demonstrations, and then learn diverse and robust grasping policies through self-exploration. The resulting policy can grasp challenging objects in various environments with an off-the-shelf parallel gripper. The key component is a learned intention estimator, which maps gripper pose and visual sensory to a set of sub-intents covering important phases of the grasping movement. Sub-intents can be used to build an intrinsic reward to guide policy learning. The learned policy demonstrates remarkable zero-shot generalization from simulation to the real world while retaining its robustness against states that have never been encountered during training, novel objects such as protractors and user manuals, and environments such as the cluttered conveyor.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L

Network-Based Regularization for Generalized Linear Models

Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) and Ren et al.(2019) ). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses

Flipbot: Learning Continuous Paper Flipping via Coarse-to-Fine Exteroceptive-Proprioceptive Exploration

This paper tackles the task of singulating and grasping paper-like deformable objects. We refer to such tasks as paper-flipping. In contrast to manipulating deformable objects that lack compression strength (such as shirts and ropes), minor variations in the physical properties of the paper-like deformable objects significantly impact the results, making manipulation highly challenging. Here, we present Flipbot, a novel solution for flipping paper-like deformable objects. Flipbot allows the robot to capture object physical properties by integrating exteroceptive and proprioceptive perceptions that are indispensable for manipulating deformable objects. Furthermore, by incorporating a proposed coarse-to-fine exploration process, the system is capable of learning the optimal control parameters for effective paper-flipping through proprioceptive and exteroceptive inputs. We deploy our method on a real-world robot with a soft gripper and learn in a self-supervised manner. The resulting policy demonstrates the effectiveness of Flipbot on paper-flipping tasks with various settings beyond the reach of prior studies, including but not limited to flipping pages throughout a book and emptying paper sheets in a box.Comment: Accepted to International Conference on Robotics and Automation (ICRA) 202

ERRA: An Embodied Representation and Reasoning Architecture for Long-horizon Language-conditioned Manipulation Tasks

This letter introduces ERRA, an embodied learning architecture that enables robots to jointly obtain three fundamental capabilities (reasoning, planning, and interaction) for solving long-horizon language-conditioned manipulation tasks. ERRA is based on tightly-coupled probabilistic inferences at two granularity levels. Coarse-resolution inference is formulated as sequence generation through a large language model, which infers action language from natural language instruction and environment state. The robot then zooms to the fine-resolution inference part to perform the concrete action corresponding to the action language. Fine-resolution inference is constructed as a Markov decision process, which takes action language and environmental sensing as observations and outputs the action. The results of action execution in environments provide feedback for subsequent coarse-resolution reasoning. Such coarse-to-fine inference allows the robot to decompose and achieve long-horizon tasks interactively. In extensive experiments, we show that ERRA can complete various long-horizon manipulation tasks specified by abstract language instructions. We also demonstrate successful generalization to the novel but similar natural language instructions.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L

Hand grip strength should be normalized by weight not height for eliminating the influence of individual differences: Findings from a cross-sectional study of 1,511 healthy undergraduates

BackgroundHand grip strength (HGS) is a fast, useful, and inexpensive outcome predictor of nutritional status and muscular function assessment. Numerous demographic and anthropometric factors were reported to be associated with HGS, while which one or several factors produce greater variations in HGS has not been discussed in detail. This is important for answering how should HGS be normalized for eliminating the influence of individual differences in clinical practice.AimsTo compare the contribution of age, sex, height, weight, and forearm circumference (FCF) to variations in HGS based on a large-scale sample.MethodsWe enrolled 1,511 healthy undergraduate students aged 18–23 years. Age, weight, height, and sex were obtained. HGS was measured using a digital hand dynamometer, and FCF was measured at the point of greatest circumference using a soft ruler in both hands. Pearson’s or Spearman’s correlation coefficients were calculated with data of women and men separated and mixed for comparison. Partial correlation analysis and multivariate linear regression were used to compare the effect of variables on HGS.ResultsAnalysis results confirmed the correlates of higher HGS include higher height, heavier weight, being men and dominant hand, and larger FCF. The correlation between HGS and FCF was the highest, and the bivariate correlation coefficient between weight and HGS was largerata of women and men were mixed, than that between height and HGS. When data of women and men were mixed, there were moderate correlations between HGS and height and weight (r = 0.633∼0.682). However, when data were separated, there were weak correlations (r = 0.246∼0.391). Notably, partial correlation analysis revealed no significant correlation between height and HGS after eliminating the weight effect, while the correlation between weight and HGS was still significant after eliminating the height effect. Multivariate linear regression analyses revealed sex was the most significant contributor to the variation in HGS (Beta = –0.541 and –0.527), followed by weight (Beta = 0.243 and 0.261) and height (Beta = 0.102 and 0.103).ConclusionHGS and FCF reference values of healthy college students were provided. Weight was more correlate with hand grip strength, at least among the healthy undergraduates.Clinical trial registrationhttp://www.chictr.org.cn/showproj.aspx?proj=165914, identifier ChiCTR2200058586