Sequential Task Research Articles

Task-dependent coarticulation of movement sequences.

Combining individual actions into sequences is a hallmark of everyday activities. Classical theories propose that the motor system forms a single specification of the sequence as a whole, leading to the coarticulation of the different elements. In contrast, recent neural recordings challenge this idea and suggest independent execution of each element specified separately. Here, we show that separate or coarticulated sequences can result from the same task-dependent controller, without implying different representations in the brain. Simulations show that planning for multiple reaches simultaneously allows separate or coarticulated sequences depending on instructions about intermediate goals. Human experiments in a two-reach sequence task validated this model. Furthermore, in co-articulated sequences, the second goal influenced long-latency stretch responses to external loads applied during the first reach, demonstrating the involvement of the sensorimotor network supporting fast feedback control. Overall, our study establishes a computational framework for sequence production that highlights the importance of feedback control in this essential motor skill.

Sleep improves accuracy, but not speed, of generalized motor learning in young and older adults and in individuals with Parkinson's disease.

An essential aspect of motor learning is generalizing procedural knowledge to facilitate skill acquisition across diverse conditions. Here, we examined the development of generalized motor learning during initial practice-dependent learning, and how distinct components of learning are consolidated over longer timescales during wakefulness or sleep. In the first experiment, a group of young healthy volunteers engaged in a novel motor sequence task over 36 h in a two-arm experimental design (either morning-evening-morning, or evening-morning-evening) aimed at controlling for circadian confounders. The findings unveiled an immediate, rapid generalization of sequential learning, accompanied by an additional long-timescale performance gain. Sleep modulated accuracy, but not speed, above and beyond equivalent wake intervals. To further elucidate the role of sleep across ages and under neurodegenerative disorders, a second experiment utilized the same task in a group of early-stage, drug-naïve individuals with Parkinson's disease and in healthy individuals of comparable age. Participants with Parkinson's disease exhibited comparable performance to their healthy age-matched group with the exception of reduced performance in recalling motor sequences, revealing a disease-related cognitive shortfall. In line with the results found in young subjects, both groups exhibited improved accuracy, but not speed, following a night of sleep. This result emphasizes the role of sleep in skill acquisition and provides a potential framework for deeper investigation of the intricate relationship between sleep, aging, Parkinson's disease, and motor learning.

Representation Learning for Sequential Volumetric Design Tasks

Abstract Volumetric design, also called massing design, is the first and critical step in professional building design which is sequential in nature. As the volumetric design process is complex, the underlying sequential design process en- codes valuable information for designers. Many efforts have been made to automatically generate reasonable volumetric designs, but the quality of the generated design solutions varies, and evaluating a design solution requires either a prohibitively comprehensive set of metrics or expensive human expertise. While previous approaches focused on learning only the final design instead of sequential design tasks, we propose to encode the design knowledge from a collection of expert or high-performing design sequences and extract useful representations using transformer-based models. Later we propose to utilize the learned representations for crucial downstream applications such as design preference evaluation and procedural design generation. We develop the preference model by estimating the density of the learned representations whereas we train an autoregressive transformer model for sequential design generation. We demonstrate our ideas by leveraging a novel dataset of thousands of sequential volumetric designs. Our preference model can compare two arbitrarily given design sequences and is almost 90% accurate in evaluation against random design sequences. Our autoregressive model is also capable of autocompleting a volumetric design sequence from a partial design sequence.

Learning long sequences in spiking neural networks

Spiking neural networks (SNNs) take inspiration from the brain to enable energy-efficient computations. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on modern sequential tasks, as they inherit limitations from recurrent neural networks (RNNs), with the added challenge of training with non-differentiable binary spiking activations. However, a recent renewed interest in efficient alternatives to Transformers has given rise to state-of-the-art recurrent architectures named state space models (SSMs). This work systematically investigates, for the first time, the intersection of state-of-the-art SSMs with SNNs for long-range sequence modelling. Results suggest that SSM-based SNNs can outperform the Transformer on all tasks of a well-established long-range sequence modelling benchmark. It is also shown that SSM-based SNNs can outperform current state-of-the-art SNNs with fewer parameters on sequential image classification. Finally, a novel feature mixing layer is introduced, improving SNN accuracy while challenging assumptions about the role of binary activations in SNNs. This work paves the way for deploying powerful SSM-based architectures, such as large language models, to neuromorphic hardware for energy-efficient long-range sequence modelling.

Muscle spindles provide flexible sensory feedback for movement sequences.

Sensory feedback is essential for motor performance and must adapt to task demands. Muscle spindle afferents (MSAs) are a major primary source of feedback about movement, and their responses are readily modulated online by gain-controller fusimotor neurons and other mechanisms. They are therefore a powerful site for implementing flexible sensorimotor control. We recorded from MSAs innervating the jaw musculature during performance of a directed lick sequence task. Jaw MSAs encoded complex jaw-tongue kinematics. However, kinematic encoding alone accounted for less than half of MSA spiking variability. MSA representations of kinematics changed based on sequence progression (beginning, middle, or end of the sequence, or reward consumption), suggesting that MSAs are flexibly tuned across the task. Dynamic control of incoming feedback signals from MSAs may be a strategy for adaptable sensorimotor control during performance of complex behaviors.

Periodic Scheduling Optimization for Dual-Arm Cluster Tools with Arm Task and Residency Time Constraints via Petri Net Model

In order to improve quality assurance in wafer manufacturing, there are strict process requirements. Besides the well-known residency time constraints (RTCs), a dual-arm cluster tool also requires each robot arm to execute a specific set of tasks. We call such a tool an arm task-constrained dual-arm cluster tool (ATC-DACT). To do this, one of the arms is identified as the dirty one and the other as the clean one. The dirty one can deal with raw wafers, while the clean one can deal with processed wafers. This requirement raises a new problem for scheduling a cluster tool. This paper discusses the scheduling problem of ATC-DACTs with RTCs. Due to the arm task constraints, the proven, effective swap strategy is no longer applicable to ATC-DACTs, making the scheduling problem difficult. To address this problem, we explicitly describe the robot waiting as an event and build a Petri net (PN) model. Then, we propose a hybrid task sequence (HTS) as an operation strategy by combining the swap and backward strategies. Based on the HTS, the necessary and sufficient conditions for schedulability are established; also, a linear programming model is developed. We then develop an algorithm using these results to optimally schedule the system. Industrial case studies demonstrate the application of this method.

The effect of combining emphasis manipulation and simplifying conditions sequencing method in gaining expertise while utilizing whole task sequencing

The effect of combining emphasis manipulation and simplifying conditions sequencing method in gaining expertise while utilizing whole task sequencing

Optimal robot task scheduling in cluttered environments considering mechanical advantage

Abstract In various industrial robotic applications, the effective traversal of a manipulator amidst obstacles and its ability to reach specific task-points are imperative for the execution of predefined tasks. In certain scenarios, the sequence in which the manipulator reaches these task-points significantly impacts the overall cycle time required for task completion. Moreover, some tasks necessitate significant force exertion at the end-effector. Therefore, establishing an optimal sequence for the task-points reached by the end-effector’s tip is crucial for enhancing robot performance, ensuring collision-free motion and maintaining high-force application at the end-effector’s tip. To maximize the manipulator’s manipulability, which serves as a performance index for assessing its force capability, we aim to establish an optimal collision-free task sequence considering higher mechanical advantage. Three optimization criteria are considered: the cycle time, collision avoidance and the manipulability index. Optimization is accomplished using a genetic algorithm coupled with the Bump-Surface concept for collision avoidance. The effectiveness of this approach is confirmed through simulation experiments conducted in 2D and 3D environments with obstacles employing both redundant and non-redundant robots.

Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout

This paper introduces a model-based approach for training feedback controllers for an autonomous agent operating in a highly nonlinear (albeit deterministic) environment. We desire the trained policy to ensure that the agent satisfies specific task objectives and safety constraints, both expressed in Discrete-Time Signal Temporal Logic (DT-STL). One advantage for reformulation of a task via formal frameworks, like DT-STL, is that it permits quantitative satisfaction semantics. In other words, given a trajectory and a DT-STL formula, we can compute the robustness , which can be interpreted as an approximate signed distance between the trajectory and the set of trajectories satisfying the formula. We utilize feedback control, and we assume a feed forward neural network for learning the feedback controller. We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives. This poses a challenge: RNNs are susceptible to vanishing and exploding gradients, and naïve gradient descent-based strategies to solve long-horizon task objectives thus suffer from the same problems. To tackle this challenge, we introduce a novel gradient approximation algorithm based on the idea of dropout or gradient sampling. One of the main contributions is the notion of controller network dropout , where we approximate the NN controller in several time-steps in the task horizon by the control input obtained using the controller in a previous training step. We show that our control synthesis methodology, can be quite helpful for stochastic gradient descent to converge with less numerical issues, enabling scalable backpropagation over long time horizons and trajectories over high dimensional state spaces. We demonstrate the efficacy of our approach on various motion planning applications requiring complex spatio-temporal and sequential tasks ranging over thousands of time-steps.

Task design, needs analysis, and task sequencing

Task design, needs analysis, and task sequencing

Estimation of video sequence homography based on a first-order estimation method

As it is a pre-processing task, estimation of video-sequence-based homography requires low computational costs and fast evaluation. However, current algorithms for video sequence tasks are commonly based on image-pair homography and do not consider the inner properties of the video sequences. Therefore they take unnecessary computational resources. In this work, we propose a novel algorithm with a first-order estimation method to fill the gap between estimation of image-pairs and video sequence homography. By considering the continuous movement of the camera, the proposed algorithm adopts a first-order estimation to accelerate the estimation process while maintaining its robustness. Instead of extracting many image features from every frame, we demonstrate that estimating a homography matrix with pixel-based texture patterns is effective and sufficient for video sequences. Experiments show that homography estimation with simple one-dimensional texture vectors, as used in our algorithm, can surpass state-of-the-art feature-based algorithms and deep-learning-based methods. This first-order estimation method was more than 40 times faster and real-time estimation used only the CPU.

Contextual control demands determine whether stability and flexibility trade off against each other.

Cognitive stability, the ability to focus on a current task, and cognitive flexibility, the ability to switch between different tasks, are traditionally conceptualized as opposing end-points on a one-dimensional continuum. This assumption obligates a stability-flexibility trade-off - greater stability equates to less flexibility, and vice versa. In contrast, a recent cued task-switching study suggested that stability and flexibility can be regulated independently, evoking a two-dimensional perspective where trade-offs are optional (Geddert & Egner, Journal of Experimental Psychology: General, 151, 3009-3027, 2022). This raises the question of under what circumstances trade-offs occur. We here tested the hypothesis that trade-offs are guided by cost-of-control considerations whereby stability and flexibility trade off in contexts that selectively promote stability or flexibility, but not when neither or both are promoted. This proposal was probed by analyzing whether a trial-level metric of a stability-flexibility trade-off, an interaction between task-rule congruency and task sequence, varied as a function of a broader block-level context that independently varied demands on stability or flexibility by manipulating the proportion of incongruent and switch trials, respectively. In Experiment 1, we reanalyzed data from Geddert and Egner, Journal of Experimental Psychology: General, 151, 3009-3027, (2022); Experiment 2 was a conceptual replication with a design tweak that controlled for potential confounds due to local trial history effects. The experiments produced robust evidence for independent stability and flexibility adaptation, and for a context-dependent expression of trial-level stability-flexibility trade-offs that generally conformed to the cost-of-control predictions. The current study thus documents that stability-flexibility trade-offs are not obligatory but arise in contexts where either stability or flexibility are selectively encouraged.

Efficient microservices offloading for cost optimization in diverse MEC cloud networks

In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.

Character-Level Text Generation for Shakespearean Style with LSTMs

This paper presents a pioneering approach to text generation employing Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) architecture, inspired by the rich and timeless prose of William Shakespeare. The motivation stems from the enduring allure of Shakespearean language, which has captivated audiences across centuries, and the challenge of replicating itsintricate style using modern computational techniques. Our research contributes a novel methodology that leverages the capabilities of RNN LSTM networks to emulate the linguistic nuances of Shakespeare with remarkable fidelity. The paper begins by providing a comprehensive overview of RNN LSTM networks, highlighting their suitability for sequential data processing tasks and their ability to capture long-rangedependencies. A review of related work in the field sets the stage for our proposed approach, shedding light on recent advancements and methodologies employed in text generation using similar techniques. We formulate the problem by defining the mathematical framework, optimization objectives, and evaluation metrics for our proposed model. The architecture consists of three layers: the data layer for preprocessing input text data, the intelligence layer comprising multiple LSTM units for capturing different aspects of Shakespearean language, and the application layer for generating output text based on learned representations. Experimental results demonstrate the effectiveness of our approach, with evaluations conducted on a corpus of Shakespearean texts.In conclusion, our research presents a significant advancement in the field of natural language generation, opening new avenues for exploring the intersection of literature and artificial intelligence.

Exploring Quadrilaterals: An Interactive Task for 7th Grade Students Using GeoGebra Classroom

Using a Dynamic Geometry System (DGS) students can engage in a dynamic learning process that allows them to experiment, create strategies, make conjectures, argue, and deduce mathematical properties. A DGS enables the introduction of proofs, by providing visual aids. The proof of the conjectures made emerges as the next step towards formalising and understanding the contents covered and the introduction of automated deduction systems at schools can be an added value. The implementation of automated deduction systems in schools faces several challenges, such as curriculum-issues, teacher knowledge and the discrepancy between automated theorem provers and traditional practices of proving in schools. Synthetic provers based on a set of inference rules and forward chaining reasoning may provide a possible help to these challenges. With the aim of introducing formal proofs and inspiring students towards this goal, a geometric conjecture was chosen for 7th grade students to engage with, based on a rule set discussed in a previous work, “A rule based theorem prover: an introduction to proofs in secondary schools”. The study also aimed to showcase the application of a Geometry Automated Theorem Prover within the classroom setting. A sequence of tasks was created for students to complete using GeoGebra Classroom. In the first phase, they were asked to identify quadrilaterals, construct a parallelogram, and measure sides and angles until they formulated a conjecture, which was strengthened by the dynamic possibility of changing the constructed parallelogram. In the second phase, the students were provided with four rules and were asked to justify their results using these rules. By the end of these justifications, the students had a complete proof of the initial conjecture. A short video of the proof made using JGEx (Java Geometry Expert) was also shown to the students.

EXPRESS: A Machine Learning Approach to Solve the E-commerce Box-Sizing Problem

E-commerce packages are notorious for their inefficient usage of space. More than one-quarter volume of a typical e-commerce package comprises air and filler material. The inefficient usage of space significantly reduces the transportation and distribution capacity increasing the operational costs. Therefore, designing an optimal set of packaging box sizes is crucial for improving efficiency. We present the first learning-based framework to determine the optimal packaging box sizes. In particular, we propose a three-stage optimization framework that combines unsupervised learning, reinforcement learning, and tree search to design box sizes. The package optimization problem is formulated into a sequential decision-making task called the box-sizing game. A neural network agent is then designed to play the game and learn heuristic rules to solve the problem. In addition, a tree-search operator is developed to improve the performance of the learned networks. When benchmarked with company-based optimization formulation and two alternate optimization models, we find that our ML-based approach can effectively solve large-scale problems within a stipulated time. We evaluated our model on real-world datasets supplied by a large e-commerce platform. The framework is currently adopted by a large e-commerce company across its 28 fulfillment centers, which is estimated to save the company about 7.1 million USD annually. In addition, it is estimated that paper consumption will be reduced by 2080 metric tons and greenhouse gas emissions by 1960 metric tons annually. The presented optimization framework serves as a decision support tool for designing packaging boxes at large e-commerce warehouses.

Smooth joint motion planning for redundant fiber placement manipulator based on improved RRT*

In automated fiber placement (AFP), addressing the continuous motion planning challenge of redundant layup manipulators in complex environments, this paper proposes an offline redundancy optimization algorithm based on improved RRT* (Rapidly-exploring Random Trees). This algorithm maximizes the utilization of kinematic redundancy to derive smooth joint trajectories devoid of collisions and singularities. Firstly, the algorithm entails constructing a search map by eliminating joint configurations that violate constraints, and subsequently planning and optimizing the joint path by minimizing a multi-objective cost under the map constraint. Furthermore, several strategies are introduced to enhance RRT* for redundancy optimization. These strategies include a piecewise Gaussian sampling strategy (PGSS) to guide efficient tree growth within complex channels and enable joint sampling constrained by task coordinates. Additionally, the improved Steering and Local Optimization method are proposed to plan joint motion while considering intermediate task sequences. The effectiveness of the proposed algorithm is demonstrated in handling complex motion planning scenarios, such as layup involving complex path curves or dense obstacles. Experimental results validate the algorithm's capability to find feasible collision-free and singularity-free paths in relevant scenarios, provided such paths exist. Moreover, trajectory smoothness is optimized with increasing iterations.

Multiobjective optimization of collaborative robotic task sequence assignment problems under collision-free constraints

This paper proposes a multiobjective optimization approach to address the challenge of collaborative manufacturing with multiple robot arms. Given the necessity for multiple robot arms to work together, the potential for collisions between robotic motions is a significant concern, and the automated task sequence assignment for robots becomes increasingly complex. Previous research has either simplified the collision-free conditions in a limited working area, or employed a master-slave approach to obtain only a local solution. Consequently, we propose a unified global optimization approach for simultaneously addressing various collaborative manufacturing issues, including robotic task sequence assignment (RTSA), multiple inverse kinematics (IK) selection, joint-space collision-free operations and multiple manufacturing objectives. As the optimal collaborative RTSA problem is a combinatorial optimization problem with non-deterministic polynomial-time hard (NP-hard) complexity, this paper presents a hybrid nondominated sorting genetic algorithm III (NSGA-III) method that integrates a Hamming-distance-based method and a greedy strategy within NSGA-III to improve population diversity and solution quality. To validate the efficacy of the proposed approach, simulation experiments were conducted on cooperative manufacturing scenarios, with two objectives: task completion time and task load balancing. The experimental results demonstrate that the proposed approach is effective in obtaining collision-free Pareto solutions. Furthermore, the proposed hybrid NSGA-III method obtains superior solutions compared to the original method in the studied problem, as measured by two performance indices.

Learning to Move and Plan like the Knight: Sequential Decision Making with a Novel Motor Mapping.

Many skills that humans acquire throughout their lives, such as playing video games or sports, require substantial motor learning and multi-step planning. While both processes are typically studied separately, they are likely to interact during the acquisition of complex motor skills. In this work, we studied this interaction by assessing human performance in a sequential decision-making task that requires the learning of a non-trivial motor mapping. Participants were tasked to move a cursor from start to target locations in a grid world, using a standard keyboard. Notably, the specific keys were arbitrarily mapped to a movement rule resembling the Knight chess piece. In Experiment 1, we showed the learning of this mapping in the absence of planning, led to significant improvements in the task when presented with sequential decisions at a later stage. Computational modeling analysis revealed that such improvements resulted from an increased learning rate about the state transitions of the motor mapping, which also resulted in more flexible planning from trial to trial (less perseveration or habitual responses). In Experiment 2, we showed that incorporating mapping learning into the planning process, allows us to capture (1) differential task improvements for distinct planning horizons and (2) overall lower performance for longer horizons. Additionally, model analysis suggested that participants may limit their search to three steps ahead. We hypothesize that this limitation in planning horizon arises from capacity constraints in working memory, and may be the reason complex skills are often broken down into individual subroutines or components during learning.

Model-free decision-making underlies motor errors in rapid sequential movements under threat

Our movements, especially sequential ones, are usually goal-directed, i.e., coupled with task-level goals. Consequently, cognitive strategies for decision-making and motor performance are likely to influence each other. However, evidence linking decision-making strategies and motor performance remains elusive. Here, we designed a modified version of the two-step task, named the two-step sequential movement task, where participants had to conduct rapid sequential finger movements to obtain rewards (n = 40). In the shock session, participants received an electrical shock if they made an erroneous or slow movement, while in the no-shock session, they only received zero reward. We found that participants who prioritised model-free decision-making committed more motor errors in the presence of the shock stimulus (shock sessions) than those who prioritised model-based decision-making. Using a mediation analysis, we also revealed a strong link between the balance of the model-based and the model-free learning strategies and sequential movement performances. These results suggested that model-free decision-making produces more motor errors than model-based decision-making in rapid sequential movements under the threat of stressful stimuli.