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In two previous papers, we calculated the dielectrophoresis (DEP) force and corresponding trajectories of high- and low-conductance 200-µm 2D spheres in a square 1 × 1-mm chamber with plane-versus-pointed, plane-versus-plane and pointed-versus-pointed electrode configurations by applying the law of maximum entropy production (LMEP) to the system. Here, we complete these considerations for configurations with four-pointed electrodes centered on the chamber edges. The four electrodes were operated in either object-shift mode (two adjacent electrodes opposite the other two adjacent electrodes), DEP mode (one electrode versus the other three electrodes), or field-cage mode (two electrodes on opposite edges versus the two electrodes on the other two opposite edges). As in previous work, we have assumed DC properties for the object and the external media for simplicity. Nevertheless, every possible polarization ratio of the two media can be modeled this way. The trajectories of the spherical centers and the corresponding DEP forces were calculated from the gradients of the system’s total energy dissipation, described by numerically-derived conductance fields. In each of the three drive modes, very high attractive and repulsive forces were found in front of pointed electrodes for the high and low-conductance spheres, respectively. The conductance fields predict bifurcation points, watersheds, and trajectories with multiple endpoints. The high and low-conductance spheres usually follow similar trajectories, albeit with reversed orientations. In DEP drive mode, the four-point electrode chamber provides a similar area for DEP measurements as the classical plane-versus-pointed electrode chamber.

A prevalent practice among primary school teachers in certain areas within Malaysia is the selection of young children for sporting pursuits based on preference and the demographical characteristics of a child. This is due to the common perception that rural and indigenous children are engaged in more robust lifestyles compared to their urban counterpart and, hence, potentially possess better sports skills. To address the practicality of such practice, this study examined differences in fundamental motor skills between indigenous, rural and urban early school children in a selected district within peninsular Malaysia. A total of 180 early school children were randomly selected to represent the three demographic groups that participated in this study. Participants' fundamental movement skills were assessed using Ulrich Test of Gross Motor Development-2 (TGMD-2), which included measurements of locomotor and object manipulation skills. Based on both raw and standard TGMD-2 scores, the data revealed no significant differences among the three groups for locomotor skills development in both male and female participants. However, significant differences were observed for both males and females in terms of object manipulation skills development. Overall findings indicated that the urban groups recorded the lowest mean for both locomotor and object manipulation skills scores, accompanied by motor developmental delays of up to 12 months with respect to the mean chronological age. Regardless, comparison of overall gross motor development among the three groups did not achieve statistical significance. In sum, findings of the current study were consistent with the motor development literature of early school children where fundamental motor skills are reliable indicators for healthy lifestyles. However, it did not lend support to the practice of demographic-based selection for specific sporting pursuits among children of this age group.

Our ability to grab, hold, and manipulate objects involves our dexterous hands, our sense of touch, and feedback from our eyes and muscles that allows us to maintain a controlled grip. Billard and Kragic review the progress made in robotics to emulate these functions. Systems have developed from simple, pinching grippers operating in a fully defined environment, to robots that can identify, select, and manipulate objects from a random collection. Further developments are emerging from advances in computer vision, computer processing capabilities, and tactile materials that give feedback to the robot. Science , this issue p. eaat8414 Dexterous manipulation is one of the primary goals in robotics. Robots with this capability could sort and package objects, chop vegetables, and fold clothes. As robots come to work side by side with humans, they must also become human-aware. Over the past decade, research has made strides toward these goals. Progress has come from advances in visual and haptic perception and in mechanics in the form of soft actuators that offer a natural compliance. Most notably, immense progress in machine learning has been leveraged to encapsulate models of uncertainty and to support improvements in adaptive and robust control. Open questions remain in terms of how to enable robots to deal with the most unpredictable agent of all, the human.

Technologies that use stretchable materials are increasingly important, yet we are unable to control how they stretch with much more sophistication than inflating balloons. Nature, however, demonstrates remarkable control of stretchable surfaces; for example, cephalopods can project hierarchical structures from their skin in milliseconds for a wide range of textural camouflage. Inspired by cephalopod muscular morphology, we developed synthetic tissue groupings that allowed programmable transformation of two-dimensional (2D) stretchable surfaces into target 3D shapes. The synthetic tissue groupings consisted of elastomeric membranes embedded with inextensible textile mesh that inflated to within 10% of their target shapes by using a simple fabrication method and modeling approach. These stretchable surfaces transform from flat sheets to 3D textures that imitate natural stone and plant shapes and camouflage into their background environments.

Three-DOF manipulators were employed for juggling of polygonal objects in order to have full control over object's configuration. Dynamic grasp condition is obtained for the instances that the manipulators carry the object on their palms. Manipulation problem is modeled as a nonlinear optimal control problem. In this optimal control problem, time of free flight is used as a free parameter to determine throw and catch times. Cost function is selected to get maximum covered horizontal distance using minimum energy. By selecting third-order polynomials for joint motions, the problem is changed to a constrained parameter selection problem. Adaptive particle swarm optimization method is consequently employed to solve the optimization problem. Effectiveness of the optimization algorithm is verified by a set of simulations in MSC. ADAMS.

Recent technological advances enable gripper-equipped robots to perform many tasks traditionally associated with the human hand, allowing the use of grippers in a wide range of applications. Depending on the application, an ideal gripper design should be affordable, energy-efficient, and adaptable to many situations. However, regardless of the number of grippers available on the market, there are still many tasks that are difficult for grippers to perform, which indicates the demand and room for new designs to compete with the human hand. Thus, this paper provides a comprehensive review of robotic arm grippers to identify the benefits and drawbacks of various gripper designs. The research compares gripper designs by considering the actuation mechanism, degrees of freedom, grasping capabilities with multiple objects, and applications, concluding which should be the gripper design with the broader set of capabilities.

Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.

The exploration and/or manipulation of objects and materials, referred to as object-oriented play (OOP), is one of the most prominent activities children engage in during early childhood. Especially within early childhood education, it is important to be able to assess and understand OOP, its developmental trajectory, and developmental value. This can support early childhood educators to successfully guide or enrich children’s OOP, so it becomes a context in which learning can take place. During the past decades, three dominant theoretical perspectives have explained and assessed certain (developmental) aspects of OOP: (1) genetic epistemology, (2) cultural historical psychology, and (3) evolutionary psychology. After reviewing the literature concerning OOP according to each theoretical perspective, this paper aims to synthesize these existing theories into a unified theoretical framework. This theoretical framework can be a starting point for future research on OOP in early childhood (education). We answer the following research questions: Q1. What are the defining labels and features of the exploration and/or manipulation of objects and materials by children in early childhood?; Q2. What is the developmental trajectory of the exploration and/or manipulation of objects and materials by children in early childhood?; Q3. What is the developmental value of the exploration and/or manipulation of objects and materials by children in early childhood?

Vision is the main component of current robotics systems that is used for manipulating objects. However, solely relying on vision for hand−object pose tracking faces challenges such as occlusions and objects moving out of view during robotic manipulation. In this work, we show that object kinematics can be inferred from local haptic feedback at the robot−object contact points, combined with robot kinematics information given an initial vision estimate of the object pose. A planar, dual-arm, teleoperated robotic setup was built to manipulate an object with hands shaped like circular discs. The robot hands were built with rubber cladding to allow for rolling contact without slipping. During stable grasping by the dual arm robot, under quasi-static conditions, the surface of the robot hand and object at the contact interface is defined by local geometric constraints. This allows one to define a relation between object orientation and robot hand orientation. With rolling contact, the displacement of the contact point on the object surface and the hand surface must be equal and opposite. This information, coupled with robot kinematics, allows one to compute the displacement of the object from its initial location. The mathematical formulation of the geometric constraints between robot hand and object is detailed. This is followed by the methodology in acquiring data from experiments to compute object kinematics. The sensors used in the experiments, along with calibration procedures, are presented before computing the object kinematics from recorded haptic feedback. Results comparing object kinematics obtained purely from vision and from haptics are presented to validate our method, along with the future ideas for perception via haptic manipulation.

The object perception capabilities of humans are impressive, and this becomes even more evident when trying to develop solutions with a similar proficiency in autonomous robots. While there have been notable advancements in the technologies for artificial vision and touch, the effective integration of these two sensory modalities in robotic applications still needs to be improved, and several open challenges exist. Taking inspiration from how humans combine visual and haptic perception to perceive object properties and drive the execution of manual tasks, this article summarises the current state of the art of visuo-haptic object perception in robots. Firstly, the biological basis of human multimodal object perception is outlined. Then, the latest advances in sensing technologies and data collection strategies for robots are discussed. Next, an overview of the main computational techniques is presented, highlighting the main challenges of multimodal machine learning and presenting a few representative articles in the areas of robotic object recognition, peripersonal space representation and manipulation. Finally, informed by the latest advancements and open challenges, this article outlines promising new research directions.