To this day, manipulation still stands as one of the hardest challenges in robotics. In this work, we examine the board game Dr. Eureka as a benchmark to encourage further development in the field. The game consists of a race to solve a manipulation puzzle: reordering colored balls in transparent tubes, in which the solution requires planning, dexterity and agility. In this work, we present a robot (Tactical Hazardous Operations Robot 3) that can solve this problem, nicely integrating several classical and state-of-the-art techniques. We represent the puzzle states as graph and solve it as a shortest path problem, in addition to applying computer vision combined with precise motions to perform the manipulation. In this paper, we also present a customized implementation of YOLO (called YOLO-Dr. Eureka) and we implement an original neural network (NN)-based incremental solution to the inverse kinematics problem. We show that this NN outperforms the inverse of the Jacobian method for large step sizes. Albeit requiring more computation per control cycle, the larger steps allow for much larger movements per cycle. To evaluate the experiment, we perform trials against a human using the same set of initial conditions.

One of the issues that have garnered little attention, but that is nevertheless important for developing practical robots, is optimal walking conditions like power consumption during walking. The main contribution of this research is to prepare a correct walking pattern for humans who have a problem with their walking and also study the effect of average motion speed on optimal power consumption. In this study, we firstly optimize the stability and minimize the power consumption of the robot during the single support phase using parameter optimization. Our approach is based on the well-known Zero Moment Point method to calculate the stability of the proposed biped robot. Secondly, we performed experiments on healthy male, age 29 years, to analyze human walking by placing 28 markers, attached to anatomical positions and two power plates for a distance of more than one gait cycle at an average speed of 1.23 ± 0.1 m s−1 validate our results for motion analysis of correct walking ability. Our model was continuously validated by comparing the results of our empirical evaluation against the prediction of our model. The errors between experimental test and our prediction were about 4%–11% for the joint trajectories and about 0.2%–0.5% for the ground reaction forces which is acceptable for our prediction. Due to the presented model and optimized issue and predicted path, the robot can move like a person in a way that has maximum stability along with the minimum power consumption. Finally, the robot was able to walk like a specific person that we considered. This study is a case study and also can be generalized to all samples and can perform these procedures to another person’s with different features.

Motivated by the classical De Bruijn's identity for the additive Gaussian noise channel, in this paper we consider a generalized setting where the channel is modelled via stochastic differential equations driven by fractional Brownian motion with Hurst parameter H ∈ (0, 1). We derive generalized De Bruijn's identity for Shannon entropy and Kullback–Leibler divergence by means of Itô's formula, and present two applications. In the first application we demonstrate its equivalence with Stein's identity for Gaussian distributions, while in the second application, we show that for H ∈ (0, 1/2], the entropy power is concave in time while for H ∈ (1/2, 1) it is convex in time when the initial distribution is Gaussian. Compared with the classical case of H = 1/2, the time parameter plays an interesting and significant role in the analysis of these quantities.

This paper deals with the problem of the formation control of nonholonomic mobile robots in the leader–follower scenario without considering the leader information, as a result of its velocity and position. The kinematic model is reformulated as a formation model by incorporating the model uncertainties and external disturbance. The controller is presented in the two-step process. Firstly, the tracking problem is taken into consideration, which can be used as a platform to design a controller for the multi-agents. The proposed controller is designed based on a non-singular fast terminal sliding mode controller (FTSMC), which drives the tracking error to zero in finite time. It not only ensures the tracking but also handles the problem related to non-singularities. Moreover, the design control scheme is modified using high-gain observer to resolve the undefined fluctuations due to man-made errors in sensors. Secondly, the multi-agent tracking problem is considered; hence, a novel formation control is designed using FTSMC, which ensures the formation pattern as well as tracking. Furthermore, the obstacle avoidance algorithm is incorporated to avoid the collision, inside the region of interest. With the Lyapunov analysis, the stability of the proposed algorithm is verified. As a result, simulated graphs are shown to prove the efficacy of the proposed control scheme.

Intelligent agents built on the basis of the BDI (belief–desire–intention) architecture are known as BDI agents. Currently, due to the increasing importance given to the affective capacities, they have evolved giving way to the BDI emotional agents. These agents are generally characterized by affective states such as emotions, mood or personality but sometimes also by affective capacities such as empathy or emotional regulation. In the paper, a review of the most relevant proposals to include emotional aspects in the design of BDI agents is presented. Both BDI formalizations and BDI architecture extensions are covered. From the review, common findings and good practices modeling affect, empathy and regulatory capacities in BDI agents, are extracted. In spite of the great advance in the area several, open questions remain and are also discussed in the paper.

The extraction and processing of temporal expressions (TEs) in textual documents have been extensively studied in several domains; however, for the legal domain it remains an open challenge. This is possibly due to the scarcity of corpora in the domain and the particularities found in legal documents that are highlighted in this paper. Considering the pivotal role played by temporal information when it comes to analyzing legal cases, this paper presents TempCourt, a corpus of 30 legal documents from the European Court of Human Rights, the European Court of Justice, and the United States Supreme Court with manually annotated TEs. The corpus contains two different temporal annotation sets that adhere to the TimeML standard, the first one capturing all TEs and the second dedicated to TEs that are relevant for the case under judgment (thus excluding dates of previous court decisions). The proposed gold standards are subsequently used to compare ten state-of-the-art cross-domain temporal taggers, and to identify not only the limitations of cross-domain temporal taggers but also limitations of the TimeML standard when applied to legal documents. Finally, the paper identifies the need for dedicated resources and the adaptation of existing tools, and specific annotation guidelines that can be adapted to different types of legal documents.

This article explores conditionals expressing that the antecedent makes a difference for the consequent. A ‘relevantised’ version of the Ramsey Test for conditionals is employed in the context of the classical theory of belief revision. The idea of this test is that the antecedent is relevant to the consequent in the following sense: a conditional is accepted just in case (i) the consequent is accepted if the belief state is revised by the antecedent and (ii) the consequent fails to be accepted if the belief state is revised by the antecedent’s negation. The connective thus defined violates almost all of the traditional principles of conditional logic, but it obeys an interesting logic of its own. The article also gives the logic of an alternative version, the ‘Dependent Ramsey Test,’ according to which a conditional is accepted just in case (i) the consequent is accepted if the belief state is revised by the antecedent and (ii) the consequent is rejected (e.g., its negation is accepted) if the belief state is revised by the antecedent’s negation. This conditional is closely related to David Lewis’s counterfactual analysis of causation.

In recent years, we have seen several new models of dependent type theory extended with some form of modal necessity operator, including nominal type theory, guarded and clocked type theory and spatial and cohesive type theory. In this paper, we study modal dependent type theory: dependent type theory with an operator satisfying (a dependent version of) the K axiom of modal logic. We investigate both semantics and syntax. For the semantics, we introduce categories with families with a dependent right adjoint (CwDRA) and show that the examples above can be presented as such. Indeed, we show that any category with finite limits and an adjunction of endofunctors give rise to a CwDRA via the local universe construction. For the syntax, we introduce a dependently typed extension of Fitch-style modal λ-calculus, show that it can be interpreted in any CwDRA, and build a term model. We extend the syntax and semantics with universes.