Visual cue-aided human supervised robot navigation guidance in photometrically challenging environments using adaptive spatial-feature kernel-guided bilateral LPP

The nearest neighbor search is an essential operation for many computer vision, data mining, and machine learning problems. Since it is so widely used, the nearest neighbor search should be as fast as possible. This paper explores lower bound-based approaches to speed up the exact nearest neighbor search in high dimensional Euclidean space. We compute the lower bound of Euclidean Distance by using the block vectors and Cauchy–Schwartz inequality. The proposed lower bound is calculated efficiently and is close to the real Euclidean Distance. Besides, the preprocessing step of the proposal has linear time complexity. Given a query, during the procedure of identifying the nearest neighbor, our method can eliminate many expensive actual distance computations using the lower bound to approximate Euclidean Distance. In addition, we develop a multilevel lower bound strategy, which calculates the lower bound step by step and utilizes the multistep filtering mechanism to improve the searching process further. Theoretical analysis is provided to show that the proposals can guarantee to obtain the same result as the brute-force search. Comprehensive experiments on 16 public data sets collected from various domains demonstrate that our approach performs well in finding the exact nearest neighbor compared to related competitors. The experimental results also illustrate that the multilevel lower bound strategy is effective.

The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots. Human-Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated. Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics. HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7). HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation. These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC.

Classical linear regression not only lacks of the flexibility in fitting the label, but also ignores to preserve the intrinsic local geometric structure of data, which leads to overfitting. In this paper, we propose a novel discriminative regression method, called adaptive locality preserving based discriminative regression (ALPDR), to address these problems. Firstly, a locality preserving constraint regularized by the adaptive weight is introduced to preserve the intrinsic geometric structures of data, in which the similar points of the same class are adaptively pulled together by the projection. Secondly, ALPDR directly learns the discriminative target matrix from data based on the given label information, which allows more freedom in label fitting and simultaneously enlarges the margins between different classes. Thirdly, ALPDR imposes a row-sparsity constraint on the projection, which enables the method to adaptively select the most discriminative features from data such that the negative influence of noises and redundant features can be eliminated. Finally, an efficient iterative algorithm is provided to optimize the model. Extensive experiments show that the proposed method outperforms the other state-of-art methods, which proves the effectiveness of the proposed method.

Local and global regularized sparse coding for data representation

We propose a novel method to improve the performance of existing three dimensional (3D) human face recognition algorithms that employ Euclidean distances between facial fiducial points as features. We further investigate a novel 3D face recognition algorithm that employs geodesic and Euclidean distances between facial fiducial points. We demonstrate that this algorithm is robust to changes in facial expression. Geodesic and Euclidean distances were calculated between pairs of 25 facial fiducial points. For the proposed algorithm, geodesic distances and 'global curvature' characteristics, defined as the ratio of geodesic to Euclidean distance between a pairs of points, were employed as features. The most discriminatory features were selected using stepwise linear discriminant analysis (LDA). These were projected onto 11 LDA directions, and face models were matched using the Euclidean distance metric. With a gallery set containing one image each of 105 subjects and a probe set containing 663 images of the same subjects, the algorithm produced EER=1.4% and a rank 1 RR=98.64%. It performed significantly better than existing algorithms based on principal component analysis and LDA applied to face range images. Its verification performance for expressive faces was also significantly better than an algorithm that employed Euclidean distances between facial fiducial points as features.

In human–robot collaboration (HRC) scenarios with direct physical contact, accurately estimating human intentions and adjusting robot behaviors based on multimodal information is the core factors that restrict the efficiency and precision of current HRC tasks. To enhance the performance of human–robot collaboration under physical contact conditions, we propose a joint network model named ACT_force_cooperative (AFC). This model leverages force sensing information as a representation of human intent to achieve human intent prediction during physical interaction, while simultaneously capturing visual information and robot state data, thereby enabling more efficient execution of human–robot collaborative tasks with multimodal information processing. Existing HRC methods often ignore humans’ collaborative experience in the environment and fail to recognize the uniqueness of interactive force in expressing human intentions. Focusing on the special role of interactive force among various types of data in physical interaction environments, the proposed model predicts humans’ future behavioral intentions and adopts a Transformer model to realize the fusion and representation of multimodal information, thus accomplishing more accurate and effective HRC tasks. Experimental results demonstrate that, through the processing of force sensing information and fusion of multimodal data, the proposed model reduces the motion error by 44.9% and increases the effective collaborative time ratio by 20.2% compared with the baseline Action Chunk Transformer (ACT) model. This not only improves the motion accuracy of the robot in collaborative tasks but also enhances the collaborative experience of human operators.

This study proposes to improve the reliability, robustness and human-like nature of Human–Robot Collaboration (HRC). For that, the classical Perception–Action cycle is extended to a Perception–Intention–Action (PIA) cycle, which includes an Intention stage at the same level as the Perception one, being in charge of obtaining both the implicit and the explicit intention of the human, opposing to classical approaches based on inferring everything from perception. This complete cycle is exposed theoretically including its use of the concept of Situation Awareness, which is shown as a key element for the correct understanding of the current situation and future action prediction. This enables the assignment of roles to the agents involved in a collaborative task and the building of collaborative plans. To visualize the cycle, a collaborative transportation task is used as a use-case. A force-based model is designed to combine the robot’s perception of its environment with the force exerted by the human and other factors in an illustrative way. Finally, a total of 58 volunteers participate in two rounds of experiments. In these, it is shown that the human agrees to explicitly state their intention without undue extra effort and that the human understands that this helps to minimize robot errors or misunderstandings. It is also shown that a system that correctly combines inference with explicit elicitation of the human’s intention is the best rated by the human on multiple parameters related to effective Human–Robot Interaction (HRI), such as perceived safety or trust in the robot.

Phylogenetic trees define a metric space over their vertices, an observation that underlines distance-based phylogenetic inference. Several authors, including Layer and Rhodes (2017), have noted that we can embed leaves of a phylogenetic tree into high-dimensional Euclidean spaces in such a way that it minimizes the distortion of the tree distances. Jiang et al. (2021) use a deep learning approach to build a mapping from the space of sequences to the Euclidean space such that the mapped sequences accurately preserve the leaf distances on a given tree. Their tool, DEPP, uses this map to place a new query sequence onto the tree by first embedding it, an idea that was particularly promising for updating a species tree given data from a single gene despite the potential discordance of the gene tree and the species tree. In focusing on Euclidean spaces, these recent papers have ignored the strong theory that suggests hyperbolic spaces are more appropriate for embedding vertices of a tree. In this paper, we show that by moving to hyperbolic spaces and addressing challenges related to non-linearity and precision, we can reduce the distortion of distances for any given number of dimensions. The distortion of distances obtained using hyperbolic embeddings is lower than Euclidean embeddings with the same number of dimensions, both in training (backbone) and testing (query). The low-distortion distances of embeddings result in better topological accuracy in updating species trees using a single gene compared to its Euclidean counterpart. It also improves accuracy in placing queries for some datasets but not all.KeywordsPhylogenetic placementDeep learningHyperbolic spacesTree embeddingDistance-based phylogenetics

We propose a distributed rewiring model which starts with a planar graph embedded into the Euclidean space and then behaves as a distributed system, where each node is provided with a set of dynamic links. The proposed rewiring evolves through cycles, where nodes explore the network to identify possible shortcuts and rewire their dynamic links. The rewiring decisions are subject to Euclidean and geodesic distance constrains. The emerging networks were assessed through topological and robustness analyses. We found that the networks display a variety of characteristics observed in complex networks encompassing phenomena such as preferential attachment, the distinctive traits of small-world networks, the presence of community structures, and robustness against degradation process. We consider that our proposal can be applied in the design of those self-managed systems in which there is a limitation on communication resources that can be represented by the Euclidean distance and, however, the components themselves can deploy strategies to optimize the transport of information and develop tolerance before contingencies.

For a closed curve in a CAT(K) space with given circumradius and upper bound on curvature, a basic lower bound on the length is established. The inequality is sharp, assumed only when the curve is the boundary of an isometric copy of a racetrack (the convex hull of two congruent circles) from a plane of constant curvature K. Previously such a theorem was proved for Euclidean plane curves by G.D.Chakerian, H.H. Johnson, and A. Vogt, and for curves in higher dimensional Euclidean spaces by A.D. Milka. A similar theorem is proved for nonclosed curves, with a notion of breadth replacing circumradius. Thus we illustrate how singular methods can extend classical Euclidean theorems to a large class of new spaces (including Riemannian manifolds of curvature bounded above) and also give significant strengthenings even in Euclidean space.

This article deals with the construction of high-gain observers for autonomous polynomial dynamical systems. In contrast to the usual approach, the system’s state is embedded into a higher dimensional Euclidean space. The observer state will be contained in said Euclidean space, which has usually higher dimension than the system’s state space. Due to this embedding it is possible to avoid singularities in the observation matrix. For some systems this even allows constructing global observers in a structured way, which would not be possible in the lower-dimensional case. Finally, the state estimate in the original coordinates can be obtained by a projection. The proposed method is applied on some example systems.

We propose a new kernel entropy component analysis approach to object recognition with image set by fusing the information entropy. Since the geometry of Symmetric Positive Definite (SPD) matrices, we model the image set with its covariance matrix (nonsingular). Thus the object recognition with image set can be model as classifying problem on the riemannian manifold space. Given the proper kernel function derived from efficient metric for SPD matrices, points lie on manifold space represented by covariance matrices can be cast into high dimensional euclidean space. In this euclidean space, traditional methods used for dimensional reduction can be applied directly. Accounting for the measurability to information of the Renyi entropy, we fuse the information entropy to the dimensional reduction progress of our method. Similar to Kernel Principal Component Analysis (KPCA), the method accomplish data dimensionality reduction by projection onto a subset of entropy preserving KPCA axes. But this subset does not need to correspond to the top eigenvalues of the kernel matrix. The positive results of experiment on datasets demonstrated effectiveness of our method.

Discriminative feature extraction based on sparse and low-rank representation

Unsupervised feature selection always occupies a key position as a preprocessing in the tasks of classification or clustering due to the existence of extra essential features within high-dimensional data. Although lots of efforts have been made, the existing methods neglect to consider the redundancy of features, and thus select redundant features. In this brief, by virtue of a generalized uncorrelated constraint, we present an improved sparse regression model [generalized uncorrelated regression model (GURM)] for seeking the uncorrelated yet discriminative features. Benefited from this, the structure of data is kept in the Stiefel manifold, which avoids the potential trivial solution triggered by a conventional ridge regression model. Besides that, the uncorrelated constraint equips the model with the closed-form solution. In addition, we also incorporate a graph regularization term based on the principle of maximum entropy into the GURM model (URAFS), so as to embed the local geometric structure of data into the manifold learning. An efficient algorithm is designed to perform URAFS by virtue of the existing generalized powered iteration method. Extensive experiments on eight benchmark data sets among seven state-of-the-art methods on the task of clustering are conducted to verify the effectiveness and superiority of the proposed method.

The embedding problem for a three-parametric family of homogeneous three-spaces into a higher-dimensional Euclidean space is considered. These three-spaces occur as space sections in cosmological models. After general consideration a certain two-parametric family is embedded into a five-dimensional Euclidean space, deferring the solution of the general case to later papers.