Placement Task Research Articles

Anticipatory postural adjustment deficits in children with developmental coordination disorder during a self-induced prehension task while standing on one leg

Effective postural control is essential for motor skill development, yet the specific nature of anticipatory control in children with Developmental Coordination Disorder (DCD) remains poorly understood for complex or dynamic stability tasks. This study investigated anticipatory postural adjustments (APA) during a self-initiated dynamic stability task. The Can Placement Task (CPT)—a self-initiated dynamic stability task—was performed by 23 children with DCD and 30 typically developing (TD) children aged 9–12 years. The task involved standing on one leg while also repositioning a can on the floor. Center of pressure (COP) movement was recorded by two force platforms during the five phases of the movement. The ground reaction force measured external support during both descent to pick up the can and ascent after replacing the can. The study used a mixed-design approach with group (DCD, TD) as a between-subject factor and condition (can position close or far) and phase of movement as within-subject. Distinct movement control characteristics were shown for children with DCD including a greater range of COP movement and higher COP velocity in the anterior-posterior direction prior to movement initiation compared with TD. The DCD group also relied more on external support during both the downward and upward phases of the CPT and needed more trials to complete the task. Only two significant interaction effects involving Group and the within-subject factors emerged. Children with DCD swayed significantly more at specific phases of the task, especially when coming up and restoring balance, and did not adapt COP velocity as a function of reaching distance. Dynamic control of posture in children with DCD is impaired as they struggle to generate the effective APAs necessary to maintain dynamic stability which leads to greater reliance on external support and more corrective movements. The CPT provides a valuable assessment of posture and dynamic balance control during a complex prehension movement performed on one leg; the task highlights distinct movement patterns between children with and without DCD.

Astrocytic glutamate transport is essential for the memory-enhancing effects of 17β-estradiol in ovariectomized mice

Infusion of 17β-estradiol (E2) into the dorsal hippocampus (DH) of ovariectomized (OVX) mice enhances memory consolidation, an effect that depends on rapid phosphorylation of extracellular signal-regulated kinase (ERK) and Akt. Astrocytic glutamate transporter 1 (GLT-1) modulates neurotransmission via glutamate uptake from the synaptic cleft. However, little is known about the contribution of DH astrocytes, and astrocytic glutamate transport, to the memory-enhancing effects of E2. This study was designed to test whether DH astrocytes contribute to estrogenic modulation of memory consolidation by determining the extent to which DH GLT-1 is necessary for E2 to enhance memory in object recognition and object placement tasks and trigger rapid phosphorylation events in DH astrocytes. OVX female mice were bilaterally cannulated into the DH or the DH and dorsal third ventricle (ICV). Post-training DH infusion of the GLT-1 inhibitor dihydrokainic acid (DHK) dose-dependently impaired memory consolidation in both tasks. Moreover, the memory-enhancing effects of ICV-infused E2 in each task were blocked by DH DHK infusion. E2 increased p42 ERK and Akt phosphorylation in DH astrocytes, and these effects were blocked by DHK. Results suggest the necessity of DH GLT-1 activity for object and spatial memory consolidation, and for E2 to enhance consolidation of these memories and to rapidly activate cell signaling in DH astrocytes. Findings indicate that astrocytic function in the DH of OVX females is necessary for memory formation and is regulated by E2, and suggest an essential role for DH astrocytic GLT-1 activity in the memory-enhancing effects of E2.

Fruit flexible collecting trajectory planning based on manual skill imitation for grape harvesting robot

The flexible collection is vital for the intelligent grape-picking robot. In view of the reliable operation method obtained by long-term training of human prior skill, a fruit flexible collecting trajectory planning method based on manual skill imitation for grape harvesting robot is proposed. The method involves capturing manual teaching trajectory data using a motion capture system, preprocessing the data, extracting features from multiple teaching trajectories, and forming a probability distribution through Gaussian mixture model – Gaussian mixture regression (GMM-GMR). And combined with the key point of manual operation trajectory, the general trajectory generated by GMR is segmented and further imitated by kernelized movement primitives (KMP) to obtain the reference trajectory, respectively. An optimization method for hyperparameter adaptation KMP (O-KMP) was proposed to meet the trajectory fitting effect of multiple key points. Mean square error (MSE) was used to evaluate the deviation of the trajectory from the reference trajectory. The partial optimal trajectory is selected and integrated into a single trajectory. Two experiments were conducted to investigate imitation: For the same starting and ending points task, the MSE of the trajectory generated by O-KMP decreased by 15.274% compared to the original fixed hyperparameter KMP. For different placement tasks, the MSE of the trajectory generated by the O-KMP decreased by 7.296% compared to the original KMP. Finally, the robot arm of the actuator visually presents different task states for the compliant placement of fresh grapes.

Nearest neighbor future captioning: generating descriptions for possible collisions in object placement tasks

Domestic service robots (DSRs) that support people in everyday environments have been widely investigated. However, their ability to predict and describe future risks resulting from their own actions remains insufficient. In this study, we focus on the linguistic explainability of DSRs. Most existing methods do not explicitly model the region of possible collisions; thus, they do not properly generate descriptions of these regions. In this paper, we propose the Nearest Neighbor Future Captioning Model that introduces the Nearest Neighbor Language Model for future captioning of possible collisions, which enhances the model output with a nearest neighbors retrieval mechanism. Furthermore, we introduce the Collision Attention Module that attends regions of possible collisions, which enables our model to generate descriptions that adequately reflect the objects associated with possible collisions. To validate our method, we constructed a new dataset containing samples of collisions that can occur when a DSR places an object in a simulation environment. The experimental results demonstrated that our method outperformed baseline methods, based on the standard metrics. In particular, on CIDEr-D, the baseline method obtained 25.09 points, whereas our method obtained 33.08 points.

Influence of gaze, vision, and memory on hand kinematics in a placement task.

People usually reach for objects to place them in some position and orientation, but the placement component of this sequence is often ignored. For example, reaches are influenced by gaze position, visual feedback, and memory delays, but their influence on object placement is unclear. Here, we tested these factors in a task where participants placed and oriented a trapezoidal block against two-dimensional (2-D) visual templates displayed on a frontally located computer screen. In experiment 1, participants matched the block to three possible orientations: 0° (horizontal), +45° and -45°, with gaze fixated 10° to the left/right. The hand and template either remained illuminated (closed-loop), or visual feedback was removed (open-loop). Here, hand location consistently overshot the template relative to gaze, especially in the open-loop task; likewise, orientation was influenced by gaze position (depending on template orientation and visual feedback). In experiment 2, a memory delay was added, and participants sometimes performed saccades (toward, away from, or across the template). In this task, the influence of gaze on orientation vanished, but location errors were influenced by both template orientation and final gaze position. Contrary to our expectations, the previous saccade metrics also impacted placement overshoot. Overall, hand orientation was influenced by template orientation in a nonlinear fashion. These results demonstrate interactions between gaze and orientation signals in the planning and execution of hand placement and suggest different neural mechanisms for closed-loop, open-loop, and memory delay placement.NEW & NOTEWORTHY Eye-hand coordination studies usually focus on object acquisition, but placement is equally important. We investigated how gaze position influences object placement toward a 2-D template with different levels of visual feedback. Like reach, placement overestimated goal location relative to gaze and was influenced by previous saccade metrics. Gaze also modulated hand orientation, depending on template orientation and level of visual feedback. Gaze influence was feedback-dependent, with location errors having no significant effect after a memory delay.

Guiding real-world reinforcement learning for in-contact manipulation tasks with Shared Control Templates

The requirement for a high number of training episodes has been a major limiting factor for the application of Reinforcement Learning (RL) in robotics. Learning skills directly on real robots requires time, causes wear and tear and can lead to damage to the robot and environment due to unsafe exploratory actions. The success of learning skills in simulation and transferring them to real robots has also been limited by the gap between reality and simulation. This is particularly problematic for tasks involving contact with the environment as contact dynamics are hard to model and simulate. In this paper we propose a framework which leverages a shared control framework for modeling known constraints defined by object interactions and task geometry to reduce the state and action spaces and hence the overall dimensionality of the reinforcement learning problem. The unknown task knowledge and actions are learned by a reinforcement learning agent by conducting exploration in the constrained environment. Using a pouring task and grid-clamp placement task (similar to peg-in-hole) as use cases and a 7-DoF arm, we show that our approach can be used to learn directly on the real robot. The pouring task is learned in only 65 episodes (16 min) and the grid-clamp placement task is learned in 75 episodes (17 min) with strong safety guarantees and simple reward functions, greatly alleviating the need for simulation.

Exploring Failure Cases in Multimodal Reasoning About Physical Dynamics

In this paper, we present an exploration of LLMs' abilities to problem solve with physical reasoning in situated environments. We construct a simple simulated environment and demonstrate examples of where, in a zero-shot setting, both text and multimodal LLMs display atomic world knowledge about various objects but fail to compose this knowledge in correct solutions for an object manipulation and placement task. We also use BLIP, a vision-language model trained with more sophisticated cross-modal attention, to identify cases relevant to object physical properties that that model fails to ground. Finally, we present a procedure for discovering the relevant properties of objects in the environment and propose a method to distill this knowledge back into the LLM.

Vision-based Robotic Arm Control for Screwdriver Bit Placement Tasks

Vision-based Robotic Arm Control for Screwdriver Bit Placement Tasks

Virtual measurements for exterior vibro-acoustic problems using experimental modal models

This paper presents a virtual sensor scheme for vibroacoustic radiation applications based on experimental modal state-space models and sparse measurements. A Kalman filter is used for the estimation combining data from the acoustical and the structural domain together with the numerical modal process model. It is proposed to use Gramians, as an energy-related measure of a system’s observability which enables assessing the observability of the coupled vibroacoustic system in the modal state-space domain and the physical state domain counterpart. This Gramian-based observability metric reveals modes or states of high observability, which ensure good estimation results in the subsequent estimation. The proposed method is validated using an industrial test setup for which, due to its high complexity, modelling with first-principle techniques is not feasible. The estimation results demonstrate improved performance compared to a pure forward use of the identified model, indicating the effectiveness of the virtual sensor approach. Additionally, the newly proposed Gramian-based observability criterion outperforms state-of-the-art metrics for sensor placement in terms of estimation accuracy for particular states of interest, as demonstrated in a sensor placement task. Overall, this paper provides a promising approach for virtual sensing for vibroacoustic applications, enabling accurate and efficient system monitoring and control.

Thinking on your feet: Anticipatory foot placements in repeated bimanual object displacements

Effective handling of objects requires proper use of the hands. If the object handling is done while standing or walking, it also requires proper use of the feet. We asked how people position their feet to meet future and ongoing object-handling demands. In previous research on this topic, participants walked to a table and picked up an object for a single displacement from one place to another. These studies shed light on sensitivity to kinematics but, strictly speaking, may not have revealed anything about sensitivity to dynamics. In the present study, we asked participants to walk to a table to move an object back and forth over different distances and at different rates. Prior to walking to the table, participants had full knowledge of what the task would be. By using a rhythmic rather than discrete object placement task, we could analyze participants' sensitivity to dynamics as well as kinematics. Consistent with our expectation that participants would tune their foot separations to demands related to dynamics, we found that stance width was wider for long than for short object displacements and was more pronounced for high displacement rates than for low displacement rates. Also consistent with our expectations about planning, these effects were evident as soon as participants reached the table. Our results add to the limited research on coordinated action of the hands and feet in purposeful object manipulation.

Efficient Object Placement via FTOPNet

Image composition involves the placement of foreground objects at an appropriate scale within a background image to create a visually realistic composite image. However, manual operations for this task are time-consuming and labor-intensive. In this study, we propose an efficient method for foreground object placement, comprising a background feature extraction module (BFEM) designed for background images and a foreground–background cross-attention feature fusion module (FBCAFFM). The BFEM is capable of extracting precise and comprehensive information from the background image. The fused features enable the network to learn additional information related to foreground–background matching, aiding in the prediction of foreground object placement and size. Our experiments are conducted using the publicly available object placement assessment (OPA) dataset. Both quantitative and visual results demonstrate that FTOPNet effectively performs the foreground object placement task and offers a practical solution for image composition tasks.

Coordination control of the automated fiber placement system using photogrammetry-based leader–follower approach

The use of multiple robots in an automated fiber placement system (AFPS) makes it possible to manufacture composite structures with complex shapes. The robots in such system usually adopt independent trajectory planning and control methods. This makes the AFPS lack coordination, and rely heavily on the high-precision calibration of the system when completing complex fiber placement tasks. In this article, a novel photogrammetry-based leader–follower (PBLF) approach is proposed for a particular AFPS which consists of a serial manipulator and a parallel robot to improve its accuracy and coordination. The approach is composed of an offline trajectory planning module, a leader module, and a follower module. The trajectory planning algorithm in the offline trajectory planning module solves the problem of robot workspace constraints and mandrel occlusion in the fiber placement tasks for complex structure. Besides, a novel photogrammetry-based pose tracking method is proposed to make the follower robot track its desired relative trajectory with respect to the moving reference frame and keep the coordination of the two robots. The theoretical stability of the proposed approach is proved. The results of experiments indicate that the proposed approach can reduce the average mean absolute errors of the relative pose in position and orientation direction by 62.15% and 46.42%, respectively. While the corresponding average root mean square errors are reduced by 61.20% and 45.26%, respectively. Therefore, compared with Non-Coordination approach, the PBLF approach can effectively improve the coordination of the two robots.

Robotic System for Post Office Package Handling

Parcel sorting is becoming a significant challenge for delivery distribution centers and is mostly automated by using high-throughput sorting machinery, but manual work is still used to feed these machines by placing the parcels on the conveyor belt. In this paper, an AI-based robotic solution that automates the parcel placement task was developed. The architecture of the proposed system along with methods on how to implement it are described by using the currently available hardware and software components. The described choices lead to a well-functioning system and the gained insights will facilitate building similar systems for parcel delivery automation.

Collaboration-Aware Hit Song Prediction

In a streaming-oriented era, predicting which songs will be successful is a significant challenge for the music industry. Indeed, there are many efforts in determining the driving factors that contribute to a song’s success, and one potential solution could be incorporating artistic collaborations, as it allows for a wider audience reach. Therefore, we propose a multi-perspective approach that includes collaboration between artists as a factor for hit song prediction. Specifically, by combining online data from Billboard and Spotify, we tackle the problem as both classification and hit song placement tasks, applying five different model variants. Our results show that relying only on music-related features is not enough, whereas models that also consider collaboration features produce better results.

Membrane receptors mediate the rapid facilitation of short-term memory by estradiol in the dorsal hippocampus of female mice.

Estrogens play a key role in learning and memory via delayed genomic and early-onset rapid mechanisms. Systemic treatment with 17β-estradiol (E2) rapidly facilitates object recognition, social recognition and object placement short-term memory in ovariectomized female mice within a timescale of only 40 min following administration. The dorsal hippocampus is one critical site of rapid estrogenic effects. Estrogen receptors (ER) are located in the cell nucleus, cytoplasm and membrane. Membrane ERs alone can mediate the rapid facilitation of long-term memory consolidation by estrogens. This study determined the role of membrane ERs in the rapid effects of 17-β estradiol (E2) on short-term memory within the dorsal hippocampus of ovariectomized mice. We infused E2 conjugated to bovine serum albumin (BSA-E2) that prevents it from crossing the cell membrane and found that the rapid facilitation by E2 of short-term memory in the social recognition, object recognition and object placement tasks is mediated by membrane ERs, independently of intracellular receptors.

Sonification as a reliable alternative to conventional visual surgical navigation

Despite the undeniable advantages of image-guided surgical assistance systems in terms of accuracy, such systems have not yet fully met surgeons’ needs or expectations regarding usability, time efficiency, and their integration into the surgical workflow. On the other hand, perceptual studies have shown that presenting independent but causally correlated information via multimodal feedback involving different sensory modalities can improve task performance. This article investigates an alternative method for computer-assisted surgical navigation, introduces a novel four-DOF sonification methodology for navigated pedicle screw placement, and discusses advanced solutions based on multisensory feedback. The proposed method comprises a novel four-DOF sonification solution for alignment tasks in four degrees of freedom based on frequency modulation synthesis. We compared the resulting accuracy and execution time of the proposed sonification method with visual navigation, which is currently considered the state of the art. We conducted a phantom study in which 17 surgeons executed the pedicle screw placement task in the lumbar spine, guided by either the proposed sonification-based or the traditional visual navigation method. The results demonstrated that the proposed method is as accurate as the state of the art while decreasing the surgeon’s need to focus on visual navigation displays instead of the natural focus on surgical tools and targeted anatomy during task execution.

An Optimal Scheme for Installation of PMUs and IEDs to Reinforce Electricity Market Immunity Against Data Attacks in Smart Grid

An intruder can disrupt the electricity market operation by injecting malicious data into the sensor measurements to induce profit/loss to a utility/consumer. Robustness in the electricity market operation against false data injection attacks (FDIAs) can be achieved by optimal deployment of phasor measurement units (PMUs) and intelligent electronic devices (IEDs). The nonlinear relationship between sensor measurements, state variables, and nodal electricity price, along with the financial constraints associated with deploying high-cost measurement devices, increases the complexity of solving the FDIA resilient sensor placement task. The solution demands a tradeoff between the financial implications of installing PMUs/IEDs and the nodal price deviation during FDIA. To overcome the above challenges, the task of identifying strategic sensor locations has been formulated as a binary optimization problem. The solution of the problem derived using binary Harris hawks optimization identifies the most vulnerable bus/branch measurements with regard to nodal price deviation due to FDIA. To generalize the applicability of the proposed scheme, the formulation has been carried out while considering the realistic constraints related to PMU channel limitations and the location of the zero injection bus. The effectiveness of the proposed work has been validated for benchmark test systems against different degrees of FDIAs.

Intersectional Identities: Making Sense of Skill Development on Clinical Placements

ObjectiveLittle is known about the influence of personal experiences on learners’ trajectories toward mastery. Newell’s theory of constraints articulates the relationship between environmental, individual, and task-related factors for skill development. This study explores how undergraduate pharmacy students experience skill development on placements and what the barriers and facilitators are within Newell’s framework. MethodsYear 3 undergraduate pharmacy students were invited to take part in focus groups exploring Newell’s theory relative to skill development. Verbatim transcripts were analyzed using an interpretive phenomenological approach. ResultsFive focus groups were conducted with 16 students. The placement task provided structure through entrustable professional activities (EPAs). The resulting skill development varied but included EPA expected behaviors and also skills for mastery, eg, self-reflection. Students’ personal identities acted as both barriers and facilitators. For example, expecting or experiencing racial microaggressions limited participation; having a local accent facilitated rapport with patients. Students worked toward integration into the community of practice (the ward), where the staff was critical to inclusion. Where students had barriers related to their identities, they found it more difficult to access the community of practice. ConclusionFactors related to the community of practice (environment), students’ identities (individual), and the EPA behaviors (task) can influence skill development during placement. For some students, these factors will be more prevalent, and elements of their identities may intersect and conflict, acting as both barriers and facilitators to skill development. Educators can consider the influence of intersectionality on student identity when designing and preparing new placements and assessing students.

Abstract TP220: The Aging Ovary Impairs Acute Stroke Outcomes

Background: Adult female rats (5-7 mos) sustain smaller infarcts after middle cerebral artery occlusion (MCAo) compared to middle-aged rats (10-12 mos), likely due to loss of estrogen. Ovariectomizing adult female rats increases infarct volume, and estrogen treatment rescues this effect. Interestingly, ovariectomizing middle-aged female rats and treating with estrogen worsens stroke outcomes. This study aims to assess what role the aging ovary plays in regulating acute stroke outcomes. Methods: Reproductively senescent (RS) underwent ovariectomy (OVX) or sham OVX surgery and allowed to recover for 3 weeks. Animals were then subjected to MCAo using endothelin-1 to induce a stroke. Sensory motor function was monitored at 2- and 5-days post-stroke using the adhesive removal task (ART) and the vibrissae-evoked forelimb placement task (VIB). Animals were terminated at 5d post-stroke, brains were excised and stained with TTC, and infarct volume was calculated and normalized to contralateral hemisphere volume. Ovaries were flash frozen, then protein was extracted and analyzed with a Milliplex 27-analyte cytokine panel. Results: OVX animals showed reduced latency in ART at both 2- and 5-days post-stroke, indicating improvement in sensory motor function, compared to intact females. Both OVX and intact animals showed impairment in the VIB task at 2-days post-stroke; however, the OVX group recovered by 5 days. Finally, there was a statistical trend towards a reduction in infarct volume in the OVX group (p=0.0862). Analysis of 27 cytokines showed that 5 cytokines (IL-1β, IL12p70, IFNγ, IL-18, and RANTES) were decreased in the aging ovary in the animals that underwent ET-1 surgery compared to shams. Such a decrease was not observed in young ovaries. A Pearson’s correlation matrix indicated significant negative correlations between 2-day ART performance and ovarian expression of IL-6 and IL-10. Conclusions: These results indicate that secretions of the aging ovary impair stroke recovery, and that the aging ovary may respond to stroke via extravasation of immune cells into circulation. These data suggest the novel hypothesis that stroke severity in aging females may be exacerbated by the aging ovary. Supported by RFAG042189 to FS and 1F31NS118970-01A to TEB.

Differentiable Constraints’ Encoding for Gradient-Based Analog Integrated Circuit Placement Optimization

Analog IC design is characterized by non-systematic re-design iterations, often requiring partial or complete layout re-design. The layout task usually starts with device placement, where the several performance figures and constraints to be met escalate its complexity immensely, and, due to the inherent tradeoffs, an “optimal” floorplan solution does not usually exist. Deep learning models are now establishing for the automation of the placement task of analog integrated circuit layout design, promising to bypass the limitations of existing approaches based on: time-consuming optimization processes with several constraints; or placement retargeting from legacy designs/templates, which rely heavily on legacy layout data. However, as the complexity of analog design cases tackled by these methodologies increases, a broader set of topological constraints must be supported to cover the different layout styles and circuit classes. Here, model-independent differentiable encodings for regularity, boundary, proximity, and symmetry island constraints are formulated for the first time in the literature, and an unsupervised loss function is used for the artificial neural network model to learn how to generate placements that follow them. The use of a deep learning model makes push-button speed placement generation possible, additionally, as only sizing data are required for its training, it discards the need to acquire legacy layouts containing insights into this vast set of, often neglected, constraints. The model is ultimately used to produce floorplans from scratch at push-button speed for real state-of-the-art analog structures, including technology nodes not used for training. A case-study comparison with a floorplan design made by a human-expert presents improvements in the fulfillment of every constraint, reaching an overall improvement of around 70%, demonstrating the approach’s value in placement design.