2022 IEEE International Conference on Development and Learning (ICDL)最新文献_第3页

Postures of the arms in the first two postnatal months 出生后头两个月的手臂姿势

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962209

Abigail DiMercurio, Cary M. Springer, D. Corbetta

{"title":"Postures of the arms in the first two postnatal months","authors":"Abigail DiMercurio, Cary M. Springer, D. Corbetta","doi":"10.1109/ICDL53763.2022.9962209","DOIUrl":"https://doi.org/10.1109/ICDL53763.2022.9962209","url":null,"abstract":"The arm postures that infants adopt in the early months of life set up a repertoire of movement patterns that may aid in the development of reaching. There is evidence of tightly flexed arm postures in the womb, but how and when arm postures change over time after birth has not been systematically documented. The present study followed infants while lying in supine weekly from 3-weeks-old until they acquired head control. We documented the frequency rate of different steady state arm postures occurring when the hands were in contact with the body or the supporting surface. Across the observed developmental period, rates of flexed arm postures at the elbow decreased and more extended elbow arm postures increased but only around the time infants began to control their head. Initial elbow flexions, with the hands mostly oriented towards the head were superseded by elbow extensions with the hands primarily oriented towards the feet. Finally, most steady state arm postures entailed the forearm resting on the supporting surface or on the body rather than being held with the elbow in the air. Together, these findings show that arm postures adopted in the womb carry over into the early postnatal months and last for several weeks before extended arm postures become more prevalent. These findings have potential implications for the development of reaching and for preparing infants to produce arm movements away from the body.","PeriodicalId":274171,"journal":{"name":"2022 IEEE International Conference on Development and Learning (ICDL)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126625153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robots or Peers? Evaluating Young Children’s Attitudes Towards Robots Using the Intergroup Contact Theory 机器人还是同伴?用群体间接触理论评价幼儿对机器人的态度

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962233

Aysel Doğan, Junko Kanero

引用次数: 0

Leveraging Developmental Psychology to Evaluate Artificial Intelligence 利用发展心理学评估人工智能

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962183

David Moore, L. Oakes, V. Romero, K. McCrink

引用次数: 1

Dynamical Driving Interactions between Human and Mentalizing-designed Autonomous Vehicle 人与智能设计自动驾驶汽车的动态驾驶交互

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962198

Yikang Zhang, Shuo Zhang, Zhichao Liang, H. Li, Haiyan Wu, Quanying Liu

{"title":"Dynamical Driving Interactions between Human and Mentalizing-designed Autonomous Vehicle","authors":"Yikang Zhang, Shuo Zhang, Zhichao Liang, H. Li, Haiyan Wu, Quanying Liu","doi":"10.1109/ICDL53763.2022.9962198","DOIUrl":"https://doi.org/10.1109/ICDL53763.2022.9962198","url":null,"abstract":"Autonomous vehicle (AV) is progressing rapidly, but there are still many shortcomings when interacting with humans. To address this problem, it is necessary to study the human behaviors in human-AV interactions, and build a predictive model of human decision-making in the interaction. In turn, modelling human behavior in human-AV interaction can help us better understand human perception of AVs and human driving strategies. In this work, we first train multi-level AV agents using reinforcement learning (RL) models to imitate three mentalizing levels (i.e., level-0, level-1, and level-2), and then design a human-AV driving task that subjects interact with each level of AV agents in a two-lane merging scenario. Both human and AV driving behaviors are recorded. We found that conservative subjects obtain more rewards because of the randomness of the RL agents. Our results indicate that (i) human driving strategies are flexible and changeable, which allows to quickly adjust the strategy to maximize the reward when gaming against AV; (ii) human driving strategies are related to mentalizing ability, and subjects with higher mentalizing scores drive more conservatively. Our study shed lights on the relationship between human driving policy and mentalizing in human-AV interactions, and it can inspire the next-generation AV.","PeriodicalId":274171,"journal":{"name":"2022 IEEE International Conference on Development and Learning (ICDL)","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127075098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Informed Sampling of Prioritized Experience Replay 优先体验重播的知情抽样

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962235

Mirza Ramicic, V. Šmídl, Andrea Bonarini

{"title":"Informed Sampling of Prioritized Experience Replay","authors":"Mirza Ramicic, V. Šmídl, Andrea Bonarini","doi":"10.1109/ICDL53763.2022.9962235","DOIUrl":"https://doi.org/10.1109/ICDL53763.2022.9962235","url":null,"abstract":"Experience replay an essential role as an information-generating mechanism plays in reinforcement learning systems that use neural networks as function approximators. It enables the artificial learning agents to store their past experiences in a sliding-window butter, effectively recycling them in the process of a continual re-training of a neural network. The intermediary process of experience caching opens a possibility for an agent to optimize the order in which the experiences are sampled from the butter. This may improve the default standard, i.e., the stochastic prioritization based on Temporal-Difference error (or TD-error), which focuses on experiences that carry more Temporal-Difference surprise for the approximator. A notion of informed prioritization is proposed, a method relying on fast on-line confidence estimates of approximator predictions in order to be able to dynamically exploit the benefits of TD-error prioritization only when its prediction confidence about the selected experiences increases. The presented informed-stochastic prioritization method of replay butter sampling, implemented as a part of standard staple Deep Q-learning algorithm outperformed the vanilla stochastic prioritization based on TD-error in 41 out of 54 trialed Atari games.","PeriodicalId":274171,"journal":{"name":"2022 IEEE International Conference on Development and Learning (ICDL)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134360730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Training Spiking Autoencoders by Truncated BPTT under Trade-offs between Simulation Steps and Reconstruction Error 基于仿真步长和重构误差权衡的截断BPTT训练尖峰自编码器

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962236

Yohei Shimmyo, Y. Okuyama, Abderazek Ben Abdallah

引用次数: 0

Exploiting a Statistical Body Model for Handover Interaction Primitives 基于统计主体模型的交接交互原语

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962217

Carlos Cardoso, Alexandre Bernardino

引用次数: 0

Feedback-Driven Incremental Imitation Learning Using Sequential VAE 基于顺序VAE的反馈驱动增量模仿学习

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962185

G. Sejnova, K. Štěpánová

引用次数: 1

Master of Puppets: Multi-modal Robot Activity Segmentation from Teleoperated Demonstrations 木偶大师:远程操作演示中的多模态机器人活动分割

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962193

Claudio Coppola, L. Jamone

{"title":"Master of Puppets: Multi-modal Robot Activity Segmentation from Teleoperated Demonstrations","authors":"Claudio Coppola, L. Jamone","doi":"10.1109/ICDL53763.2022.9962193","DOIUrl":"https://doi.org/10.1109/ICDL53763.2022.9962193","url":null,"abstract":"Programming robots for complex tasks in unstructured settings (e.g., light manufacturing, extreme environments) cannot be accomplished solely by analytical methods. Learning from teleoperated human demonstrations is a promising approach to decrease the programming burden and to obtain more effective controllers. However, the recorded demonstrations need to be decomposed into atomic actions to facilitate the representation of the desired behaviour, which can be very challenging in real-world settings. In this study, we propose a method that uses features extracted from robot motion and tactile data to automatically segment atomic actions from a teleoperation sequence. We created a publicly available dataset with demonstrations of robotic pick-and-place of three different objects in single-object and cluttered situations. We use a custom-built teleoperation system that maps the user’s hand and fingertips poses into a three-fingered dexterous robot hand equipped with tactile sensors. Our findings suggest that the proposed feature set generalises the activities in different episodes of the same object and between items of similar size. Furthermore, they suggest that tactile sensing contributes to higher performance in recognising activities within demonstrations.","PeriodicalId":274171,"journal":{"name":"2022 IEEE International Conference on Development and Learning (ICDL)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131056857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulating a Human Fetus in Soft Uterus 在软子宫中模拟人类胎儿

2022 IEEE International Conference on Development and Learning (ICDL) Pub Date : 2022-09-12 DOI: 10.1109/ICDL53763.2022.9962201

Dongmin Kim, Hoshinori Kanazawa, Y. Kuniyoshi

{"title":"Simulating a Human Fetus in Soft Uterus","authors":"Dongmin Kim, Hoshinori Kanazawa, Y. Kuniyoshi","doi":"10.1109/ICDL53763.2022.9962201","DOIUrl":"https://doi.org/10.1109/ICDL53763.2022.9962201","url":null,"abstract":"Behavioral studies suggest that human cognitive development begins in the early developmental stage. However, owing to technical and ethical difficulties, there is limited knowledge on how embodied interaction contributes to early human development. Accordingly, constructive approaches for deepening the understanding of cognitive development are gaining attention in developmental robotics and dynamic systems. In this study, we performed a biologically plausible simulation of early human development, by updating the fetal simulation model that was developed in our previous study including a musculoskeletal body and uterine environment model. First, we updated the dynamics of the joints and muscles. Furthermore, we developed a new method to create soft objects of the desired shape in a rigid body simulator to achieve a soft uterine model. Subsequently, in a new simulation, we examined the impact of the soft uterine environment on the tactile experiences and subsequent cortical learning, compared to the extant uterine model and extrauterine environment. We observed that the soft uterine environment provided frequent and varied tactile information, which facilitated cortical learning toward achieving a higher cortical response to sensory inputs. Furthermore, we demonstrated that the embodied structure induced by the tactile sensor arrangement was crucial for cortical learning. Because actual fetuses and infants participate in flexible interactions, the proposed simulation using the soft environment could illuminate developmental care in the medical field.","PeriodicalId":274171,"journal":{"name":"2022 IEEE International Conference on Development and Learning (ICDL)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123039544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1