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This paper presents a method to include unmodeled dynamics of load or a robot's end-effector into algorithms for collision detection or general understanding of a robot's operation context. The approach relies on the application of a previously developed modification of the Dynamic Time Warping algorithm, as well as a universally applicable algorithm for identifying kinematic parameters. The entire process can be applied to arbitrary robot configuration, and it does not require identification of dynamic parameters. The paper addresses the two main categories of contact tasks with unmodelled dynamics, which are determined based on whether the external contact force has a consistent profile in the end effector or base coordinate. Conclusions for representative examples analysed in the paper are applicable to tasks such as load manipulation, press bending, and crimping for the first type of forces and applications such as drilling, screwdriving, snap-fit, bolting, and riveting assembly for the latter category. The results presented in the paper are based on realistic testing with measurements obtained from an industrial robot. [ABSTRACT FROM AUTHOR]

Object tracking and pose estimation have always been challenging tasks in robotics, particularly for rotating objects. Rotating objects move quickly and with complex pose variations. In this study, we introduce a marker-based tracking and pose estimation method for rotating objects using a high-speed vision system. The method can obtain pose information at frequencies greater than 500 Hz, and can still estimate the pose when parts of the markers are lost during tracking. A robot catching experiment shows that the accuracy and frequency of this system are capable of high-speed tracking tasks. [ABSTRACT FROM AUTHOR]

Several industrial pick-and-place applications, such as collaborative assembly lines, rely on visual tracking of the parts. Recurrent occlusions are caused by the manipulator motion decrease line productivity and can provoke failures. This work provides a complete solution for maintaining the occlusion-free line of sight between a variable-pose camera and the object to be picked by a 6R manipulator that is not wrist-partitioned. We consider potential occlusions by the manipulator as well as the operator working at the assembly station. An actuated camera detects the object goal (part to pick) and keeps track of the operator. The approach consists of using the complete set of solutions obtained from the derivation of the univariate polynomial equation solution to the inverse kinematics (IK). Compared to numerical iterative solving methods, our strategy grants us a set of joint positions (posture) for each root of the equation from which we extract the best (minimizing the risks of occlusion). Our analytical-based method, integrating collision and occlusion avoidance optimizations, can contribute to greatly enhancing the efficiency and safety of collaborative assembly workstations. We validate our approach with simulations as well as with physical deployments on commercial hardware. [ABSTRACT FROM AUTHOR]

Offline reinforcement learning (RL) can learn effective policy from a fixed batch of data without interaction. However, the real-world requirements, such as better performance and high sample efficiency, put substantial challenges on current offline RL algorithms. In this paper, we propose a novel offline RL method, Constrained and Conservative Reinforcement Learning with Anderson Acceleration (CCRL-AA), which aims to enable the agent to effectively and efficiently learn from offline demonstration data. In our method, Constrained and Conservative Reinforcement Learning (CCRL) restricts the policy's actions with respect to a batch of training data and learns a conservative Q-function to make the agent effectively learn from the previously collected demonstrations. The mechanism of Anderson acceleration (AA) is integrated to speed up the learning process and improve sample efficiency. Experiments were conducted on robotic simulation tasks, and the results demonstrate that our method can efficiently learn from given demonstrations and give better performance than several other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study presents the implementation of basic nursing tasks and human subject tests with a mobile robotic platform (PR2) for hospital patients. The primary goal of this study is to define the requirements for a robotic nursing assistant platform. The overall designed application scenario consists of a PR2 robotic platform, a human subject as the patient, and a tablet for patient–robot communication. The PR2 robot understands the patient's request and performs the requested task by performing automated action steps. Two categories and three tasks are defined as: patient sitter tasks, include object fetching and temperature measurement, and patient walker tasks, including supporting the patient while they are using the walker. For this designed scenario and these tasks, human subject tests are performed with 27 volunteers in the Assistive Robotics Laboratory at the University of Texas at Arlington Research Institute (UTARI). Results and observations from human subject tests are provided. These activities are part of a larger effort to establish adaptive robotic nursing assistants (ARNA) for physical tasks in hospital environments. [ABSTRACT FROM AUTHOR]

In this paper, a robotic Multitasking Intelligent Nurse Aid (MINA) is proposed to assist nurses with everyday object fetching tasks. MINA consists of a manipulator arm on an omni-directional mobile base. Before the operation, an augmented reality interface was used to place waypoints. Waypoints can indicate the location of a patient, supply shelf, and other locations of interest. When commanded to retrieve an object, MINA uses simultaneous localization and mapping to map its environment and navigate to the supply shelf waypoint. At the shelf, MINA builds a 3D point cloud representation of the shelf and searches for barcodes to identify and localize the object it was sent to retrieve. Upon grasping the object, it returns to the user. Collision avoidance is incorporated during the mobile navigation and grasping tasks. We performed experiments to evaluate MINA's efficacy including with obstacles along the path. The experimental results showed that MINA can repeatedly navigate to the specified waypoints and successfully perform the grasping and retrieval task. [ABSTRACT FROM AUTHOR]

We propose a novel robotic system that combines both a reliable programming-based approach and a highly generalizable learning-based approach. How to design and implement a series of tasks in an atypical environment is a challenging issue. If all tasks are implemented using a programming-based approach, the development costs will be huge. However, if a learning-based approach is used, reliability is an issue. In this paper, we propose novel design guidelines that focus on the respective advantages of programming-based and learning-based approaches and select them so that they complement each other. We use a program-based approach for motions that is rough behavior and a learning-based approach for motion that is required complex interaction between robot and object of robot tasks and are difficult to achieve with a program. Our learning approach can easily and rapidly accomplish a series of tasks consisting of various motions because it does not require a computational model of an object to be designed in advance. We demonstrate a series of tasks in which randomly arranged parts are assembled using an actual robot. [ABSTRACT FROM AUTHOR]

While the robotics techniques have not developed to full automation, robot following is common and crucial in robotic applications to reduce the need for dedicated teleoperation. To achieve this task, the target must first be robustly and consistently perceived. In this paper, a robust visual tracking approach is proposed. The approach adopts a scene analysis module (SAM) to identify the real target and similar distractors, leveraging statistical characteristics of cross-correlation responses. Positive templates are collected based on the tracking confidence constructed by the SAM, and negative templates are gathered by the recognized distractors. Based on the collected templates, response fusion is performed. As a result, the responses of the target are enhanced and the false responses are suppressed, leading to robust tracking results. The proposed approach is validated on an outdoor robot-person following dataset and a collection of public person tracking datasets. The results show that our approach achieved state-of-the-art tracking performance in terms of both the robustness and AUC score. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel deep reinforcement learning (DRL) based method that is used to perform multi-robot task allocation (MRTA) and navigation in an end-to-end fashion. The policy operates in a decentralized manner mapping raw sensor measurements to the robot's steering commands without the need to construct a map of the environment. We also present a new metric called the Task Allocation Index ( T A I ), which measures the performance of a method that performs MRTA and navigation from end-to-end in performing MRTA. The policy was trained on a simulated gazebo environment. The centralized learning and decentralized execution paradigm was used for training the policy. The policy was evaluated quantitatively and visually. The simulation results showed the effectiveness of the proposed method deployed on multiple Turtlebot3 robots. [ABSTRACT FROM AUTHOR]

The success of a robotic pick and place task depends on the success of the entire procedure: from the grasp planning phase, to the grasp establishment phase, then the lifting and moving phase, and finally the releasing and placing phase. Being able to detect and recover from grasping failures throughout the entire process is therefore a critical requirement for both the robotic manipulator and the gripper, especially when considering the almost inevitable object occlusion by the gripper itself during the robotic pick and place task. With the rapid rising of soft grippers, which rely heavily on their under-actuated body and compliant, open-loop control, less information is available from the gripper for effective overall system control. Tackling on the effectiveness of robotic grasping, this work proposes a hybrid policy by combining visual cues and proprioception of our gripper for the effective failure detection and recovery in grasping, especially using a proprioceptive self-developed soft robotic gripper that is capable of contact sensing. We solved failure handling of robotic pick and place tasks and proposed (1) more accurate pose estimation of a known object by considering the edge-based cost besides the image-based cost; (2) robust object tracking techniques that work even when the object is partially occluded in the system and achieve mean overlap precision up to 80%; (3) contact and contact loss detection between the object and the gripper by analyzing internal pressure signals of our gripper; (4) robust failure handling with the combination of visual cues under partial occlusion and proprioceptive cues from our soft gripper to effectively detect and recover from different accidental grasping failures. The proposed system was experimentally validated with the proprioceptive soft robotic gripper mounted on a collaborative robotic manipulator, and a consumer-grade RGB camera, showing that combining visual cues and proprioception from our soft actuator robotic gripper was effective in improving the detection and recovery from the major grasping failures in different stages for the compliant and robust grasping. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this investigation was to elucidate the role of button-response complexity to targets in a response inhibition task.Background: Response inhibition is the ability to correctly inhibit an overt response to a target. The U.S. military is actively pursuing development of armed, combat robots as a force multiplier, which may present challenges to operators of combat robots in the form of response inhibition errors.Method: A total of 15 participants completed two 51-min versions of a modified sustained attention to response task (SART). Participants were outfitted with an electrocardiogram to index heart-rate variability and completed the NASA-Task Load Index (NASA-TLX) to index workload.Results: The results demonstrated that the complex SART reduced errors of commission (4%) and slowed response times (874 ms) to correct Go targets relative to the simple SART (14%, 739 ms). The NASA-TLX did not show differences between the modified SARTs; however, heart-rate variability did demonstrate that Soldiers had an increased autonomic stress response to the complex SART.Conclusion: Increasing the behavioral response requirement during a response inhibition task can decrease errors of commission; however, it comes at the cost of slower response times to target stimuli. Heart-rate variability may provide better insight into objective workload relative to subjective measures.Application: The use of complex behavioral responses may provide a viable option to reduce potential "friendly fire" or collateral damage by Soldiers remotely engaging a target-rich environment. [ABSTRACT FROM AUTHOR]

Copyright of Bajo Palabra: Journal of Philosophy is the property of Bajo Palabra: Journal of Philosophy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: The application of the proposed work is in robotics. If a certain robot operation is defined for a part of a particular object, it can be transferred to other class instances by applying the proposed method for semantic association of the object components. Most objects are composed of semantically distinctive parts that are more or less geometrically distinctive as well. Points on the object relevant for a certain robot operation are usually determined by various physical properties of the object, such as its dimensions or weight distribution, and by the purpose of object parts. A robot operation defined for a particular part of a representative object can be transferred and adapted to other instances of the same object class by detecting the corresponding components. In this paper, a method for semantic association of the object's components within the object class is proposed. It is suitable for real-time robotic tasks and requires only a few previously annotated representative models. The proposed approach is based on the component association graph and a novel descriptor that describes the geometrical arrangement of the components. The method is experimentally evaluated on a challenging benchmark dataset. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The goal of reinforcement learning is to enable an agent to learn by using rewards. However, some robotic tasks naturally specify with sparse rewards, and manually shaping reward functions is a difficult project. In this article, we propose a general and model-free approach for reinforcement learning to learn robotic tasks with sparse rewards. First, a variant of Hindsight Experience Replay, Curious and Aggressive Hindsight Experience Replay, is proposed to improve the sample efficiency of reinforcement learning methods and avoid the need for complicated reward engineering. Second, based on Twin Delayed Deep Deterministic policy gradient algorithm, demonstrations are leveraged to overcome the exploration problem and speed up the policy training process. Finally, the action loss is added into the loss function in order to minimize the vibration of output action while maximizing the value of the action. The experiments on simulated robotic tasks are performed with different hyperparameters to verify the effectiveness of our method. Results show that our method can effectively solve the sparse reward problem and obtain a high learning speed. [ABSTRACT FROM AUTHOR]

Dynamic task allocation (DTA) is a key feature in collaborative robotics. It affects organizations' profits and allows agents to perform more tasks when efficiently designed. Although some work has been done on DTA, allocating tasks dynamically in an uncertain environment between heterogeneous multi-agents has rarely been investigated. The solutions proposed so far have inefficiently managed uncertainty, and none of them has utilized the semantics of heterogeneous agents' capabilities. Studies measuring the performance of these techniques on real robots are also scarce. Therefore, this paper proposes an online DTA method, which introduces new functionalities that can be applied in a real environment. In particular, an uncertain incremental cost function is developed with a distributed semantic negotiation strategy that reflects heterogeneous capabilities without needing to communicate them. The proposed method is tested in a dynamic environment and experiments on heterogeneous real/virtual robots are conducted with different numbers of agents. Different statistical and visualization tools are used to analyze the results, including bar graphs for the waiting time metrics, histograms for the waiting time frequency, scatter plots for the result distribution and variance, and critical difference diagrams for ANOVA–Tukey results. The results indicate that the proposed DTA balances allocation quality and reliability, allowing the agents to serve targets equally without neglecting certain targets at the expense of the total performance. Evidently, updating the cost incrementally allows agents to update their allocation and choose better routes to finish the task earlier. Understanding the capability also gives priority to the capable agents that complete the task faster. [ABSTRACT FROM AUTHOR]

This paper contributes a novel framework to efficiently learn cost-to-go function representations for robotic tasks with latent modes. The proposed approach relies on the principle behind ensemble methods, where improved performance is obtained by aggregating a group of simple models, each of which can be efficiently learnedq. The maximum-entropy approximation is adopted as an effective initialization and the quality of this surrogate is guaranteed by a theoretical bound. Our approach also provides an alternative perspective to view the popular mixture of Gaussians under the framework of inverse optimal control. We further propose to enforce a dynamics on the model ensemble, using Kalman estimation to infer and modulate model modes. This allows robots to exploit the demonstration redundancy and to adapt to human interventions, especially in tasks where sensory observations are non-Markovian. The framework is demonstrated with a synthetic inverted pendulum example and online adaptation tasks, which include robotic handwriting and mail delivery. [ABSTRACT FROM AUTHOR]

While women played a pivotal role at the onset of computer programming, technology fields, including robotics, are currently gendered. Prior research fails to consider how the context of robotics activities might impact men's and women's perceptions of robotics tasks. Our study, a survey experiment of 132 men and women, seeks to understand 1) whether women and men have different interest in robotics instruction tasks, 2) whether describing a robotics instruction task scenario as feminine (teaching the robot), masculine (programming the robot), or gender neutral (training the robot) relate to women's and men's interest in robotics instruction tasks, and 3) whether perceptions that instructing robots to perform tasks is good for society or requires natural talent impacts women's interest in the robotics task more strongly than men's interest. Contrary to our hypotheses, we find that women are more interested in most of the robotics instruction tasks than are men, and that framing the robotics instruction task scenarios as feminine produces worse outcomes for women in some of the robotics tasks. We also find that both men and women who think instructing robots to perform tasks is beneficial to society are significantly more likely to have interest in the robotics tasks than those who do not hold these views. We provide possible explanations for these findings, as well as theoretical and practical implications. [ABSTRACT FROM AUTHOR]

A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization. [ABSTRACT FROM AUTHOR]

Planners developed in the Artificial Intelligence community assume that tasks in the task plans they generate will be executed predictably and reliably. This assumption provides a useful abstraction in that it lets the task planners focus on what tasks should be done, while lower-level motion planners and controllers take care of the details of how the task should be performed. While this assumption is useful in many domains, it becomes problematic when controlling physically embedded systems, where there are often delays, disturbances, and failures. The task plans do not provide enough information about allowed flexibility in task duration and hybrid state evolution. Such flexibility could be useful when deciding how to react to disturbances. An important domain where this gap has caused problems is robotics, particularly, the operation of robots in unstructured, uncertain environments. Due to the complexity of this domain, the demands of tasks to be performed, and the actuation limits of robots, knowledge about permitted flexibility in execution of a task is crucial. We address this gap through two key innovations. First, we specify a Qualitative State Plan (QSP), which supports representation of spatial and temporal flexibility with respect to tasks. Second, we extend compilation approaches developed for temporally flexible execution of discrete activity plans to work with hybrid discrete/continuous systems using a recently developed Linear Quadratic Regulator synthesis algorithm, which performs a state reachability analysis to prune infeasible trajectories, and which determines optimal control policies for feasible state regions. The resulting Model-based Executive is able to take advantage of spatial and temporal flexibility in a QSP to improve handling of disturbances. Note that in this work, we focus on execution of QSPs, and defer the problem of how they are generated. We believe the latter could be accomplished through extensions to existing task planners. [ABSTRACT FROM AUTHOR]

This paper proposes a task-oriented grasp quality metric based on distribution of task disturbance, which could be used to search for a grasp that covers the most significant part of the disturbance distribution. Rather than using a uniformly distributed task wrench space, this paper models a manipulation task with a non-parametric statistical distribution model built from the disturbance data captured during the task demonstrations. The grasp resulting from maximizing the proposed grasp quality criterion is prone to increasing the coverage of most frequent disturbances. To reduce the computational complexity of the search in a high-dimensional robotic hand configuration space, as well as to avoid the correspondence problem, the candidate grasps are computed from a reduced configuration space that is confined by a set of given thumb placements and thumb directions. The proposed approach has been tested both in simulation and on a real robotic system. In simulation, the approach was validated with a Barrett hand and a Shadow hand in several manipulation tasks. Experiments on a physical robotic platform verified the consistency between the proposed grasp metric and the success rate. [ABSTRACT FROM AUTHOR]

Industrial robot applications should be designed to allow the robot to provide the best performance for increasing throughput. In this regard, both trajectory and task order optimization are crucial, since they can heavily impact cycle time. Moreover, it is very common for a robotic application to be kinematically or functionally redundant so that multiple arm configurations may fulfill the same task at the working points. In this context, even if the working cycle is composed of a small number of points, the number of possible sequences can be very high, so that the robot programmer usually cannot evaluate them all to obtain the shortest possible cycle time. One of the most well-known problems used to define the optimal task order is the Travelling Salesman Problem (TSP), but in its original formulation, it does not allow to consider different robot configurations at the same working point. This paper aims at overcoming TSP limitations by adding some mathematical and conceptual constraints to the problem. With such improvements, TSP can be used successfully to optimize the cycle time of industrial robotic tasks where multiple configurations are allowed at the working points. Simulation and experimental results are presented to assess how cost (cycle time) and computational time are influenced by the proposed implementation. [ABSTRACT FROM AUTHOR]

The peg-in-hole task with object feature uncertain is a typical case of robotic operation in the real-world unstructured environment. It is nontrivial to realize object perception and operational decisions autonomously, under the usual visual occlusion and real-time constraints of such tasks. In this paper, a Bayesian networks-based strategy is presented in order to seamlessly combine multiple heterogeneous senses data like humans. In the proposed strategy, an interactive exploration method implemented by hybrid Monte Carlo sampling algorithms and particle filtering is designed to identify the features' estimated starting value, and the memory adjustment method and the inertial thinking method are introduced to correct the target position and shape features of the object respectively. Based on the Dempster–Shafer evidence theory (D-S theory), a fusion decision strategy is designed using probabilistic models of forces and positions, which guided the robot motion after each acquisition of the estimated features of the object. It also enables the robot to judge whether the desired operation target is achieved or the feature estimate needs to be updated. Meanwhile, the pliability model is introduced into repeatedly perform exploration, planning and execution steps to reduce interaction forces, the number of exploration. The effectiveness of the strategy is validated in simulations and in a physical robot task. [ABSTRACT FROM AUTHOR]

This study proposes a novel hybrid imitation learning (HIL) framework in which behavior cloning (BC) and state cloning (SC) methods are combined in a mutually complementary manner to enhance the efficiency of robotic manipulation task learning. The proposed HIL framework efficiently combines BC and SC losses using an adaptive loss mixing method. It uses pretrained dynamics networks to enhance SC efficiency and performs stochastic state recovery to ensure stable learning of policy networks by transforming the learner's task state into a demo state on the demo task trajectory during SC. The training efficiency and policy flexibility of the proposed HIL framework are demonstrated in a series of experiments conducted to perform major robotic manipulation tasks (pick-up, pick-and-place, and stack tasks). In the experiments, the HIL framework showed about a 2.6 times higher performance improvement than the pure BC and about a four times faster training time than the pure SC imitation learning method. In addition, the HIL framework also showed about a 1.6 times higher performance improvement and about a 2.2 times faster training time than the other hybrid learning method combining BC and reinforcement learning (BC + RL) in the experiments. [ABSTRACT FROM AUTHOR]

Manual inspection of workpieces in highly flexible production facilities with small lot sizes is costly and less reliable compared to automated inspection systems. Reinforcement Learning (RL) offers promising, intelligent solutions for robotic inspection and manufacturing tasks. This paper presents an RL-based approach to determine a high-quality set of sensor view poses for arbitrary workpieces based on their 3D computer-aided design (CAD). The framework extends available open-source libraries and provides an interface to the Robot Operating System (ROS) for deploying any supported robot and sensor. The integration into commonly used OpenAI Gym and Baselines leads to an expandable and comparable benchmark for RL algorithms. We give a comprehensive overview of related work in the field of view planning and RL. A comparison of different RL algorithms provides a proof of concept for the framework's functionality in experimental scenarios. The obtained results exhibit a coverage ratio of up to 0.8 illustrating its potential impact and expandability. The project will be made publicly available along with this article. [ABSTRACT FROM AUTHOR]

The goal of imitation learning (IL) is to enable the robot to imitate expert behavior given expert demonstrations. Adversarial imitation learning (AIL) is a recent successful IL architecture that has shown significant progress in complex continuous tasks, particularly robotic tasks. However, in most cases, the acquisition of high-quality demonstrations is costly and laborious, which poses a significant challenge for AILs. Although generative adversarial imitation learning (GAIL) and its extensions have shown that they are robust to sub-optimal experts, it is difficult for them to surpass the performance of experts by a large margin. To address this issue, in this paper, we propose a novel off-policy AIL method called robust adversarial imitation learning (RAIL). To enable the agent to significantly outperform a sub-optimal expert providing demonstrations, the hindsight idea of variable reward (VR) is first incorporated into the off-policy AIL framework. Then, a strategy called hindsight copy (HC) of demonstrations is designed to provide the discriminator and trained policy in the AIL framework with different demonstrations to maximize the use of such demonstrations and speed up the learning. Experiments were conducted on two multi-goal robotic tasks to test the proposed method. The results show that our method is not limited to the quality of expert demonstrations and can outperform other IL approaches. • An off-policy actor-critic architecture is used in the Adversarial imitation learning (AIL). • The hindsight idea of variable reward (VR) is incorporated into our off-policy AIL framework. • The strategy of hindsight copy (HC) is designed for sampling demonstrations. • The convergence analysis of the proposed method is provided. [ABSTRACT FROM AUTHOR]

It is difficult for swarm robots to allocate tasks efficiently by self-organization in a dynamic unknown environment. The computational cost of swarm robots will be significantly increased for large-scale tasks, and the unbalanced task allocation of robots will also lead to a decrease in system efficiency. To address these issues, we propose a dynamic task allocation method of swarm robots based on optimal mass transport theory. The problem of large-scale tasks is solved by grouping swarm robots to complete regional tasks. The task reallocation mechanism realizes the balanced task allocation of individual robots. This paper solves the symmetric assignment between robot and task and between the robot groups and the regional tasks. Our simulation and experimental results demonstrate that the proposed method can make the swarm robots self-organize to allocate large-scale dynamic tasks effectively. The tasks can also be balanced allocated to each robot in the swarm of robots. [ABSTRACT FROM AUTHOR]

Research has shown that educational robotics can be an effective tool to increase students' acquisition of knowledge in the subjects of science, technology, engineering, and mathematics and promote, at the same time, a progression in the development of computational thinking (CT) skills in K–12 (kindergarten to 12th grade) education. Within this research field, the present study first sought to assess the effect of a robotics laboratory on the acquisition of CT-related skills in primary school children. The study also aimed to compare the magnitude of the effect of the laboratory across third- and fourth-grade students. For the purpose of the study, a quasi-experimental post-test-only design was adopted, and a group of 51 students, from third- and fourth-grade classrooms, participating in the robotics laboratories, were compared to a control group of 32 students from classrooms of the same grades. A set of Bebras tasks was selected as an overall measure of CT skills and was administered to children in both the intervention and control groups. Overall, the results showed that programming robotics artefacts may exert a positive impact on students' learning of computational thinking skills. Moreover, the effect of the intervention was found to be greater among third-grade children. [ABSTRACT FROM AUTHOR]

One of the essential abilities in animals is to detect novelties within their environment. From the computational point of view, novelty detection consists of finding data that are different in some aspect to the known data. In robotics, researchers have incorporated novelty modules in robots to develop automatic exploration and inspection tasks. The visual sensor is one of the preferred sensors to perform this task. However, there exist problems as illumination changes, occlusion, and scale, among others. Besides, novelty detectors vary their performance depending on the specific application scenario. In this work, we propose a visual novelty detection framework for specific exploration and inspection tasks based on evolved novelty detectors. The system uses deep features to represent the visual information captured by the robots and applies a global optimization technique to design novelty detectors for specific robotics applications. We verified the performance of the proposed system against well-established state-of-the-art methods in a challenging scenario. This scenario was an outdoor environment covering typical problems in computer vision such as illumination changes, occlusion, and geometric transformations. The proposed framework presented high-novelty detection accuracy with competitive or even better results than the baseline methods. [ABSTRACT FROM AUTHOR]