A Survey of Embodied AI: From Simulators to Research Tasks

Duan, Jiafei; Jian, Samson Yu Bai; Tan, Hui Li; Zhu, Hongyuan; Tan, Cheston

doi:10.1109/tetci.2022.3141105

Cited by 134 publications

(53 citation statements)

References 84 publications

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“…Other factors contributing to m-commerce development include expanding the Internet of Things and artificial intelligence, especially among younger generations. Both innovations can be incorporated to enhance users' experience [40][41][42] and build their loyalty towards brands.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Adoption of Mobile Technology for Commerce by Generation Z

et al. 2022

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E-commerce has gained momentum with the rapid development of technology, and nowadays, we are permanently connected, with constant access to information and a wide range of products. Not only does a desktop computer offer us this possibility, but the latest-generation tablets and mobile phones create a broad framework. This paper investigates Romanian consumers’ attitudes towards adopting mobile technology for commerce (m-commerce), taking into account its development in the last few years, especially among younger generations. The main objectives of the research are to identify the preference for m-commerce use among Generation Z, establish the ways and the devices used by Gen Z individuals to inform about the products and services and order them, and analyze the factors influencing the use of m-commerce applications. The research methodology consists of conducting an empirical analysis using a distributed survey among youngsters from Generation Z in Romania. We used descriptive statistics, such as the analysis of frequency and the mean of variables, artificial neural network analysis (ANN), and multivariate analysis of variance (MANOVA), to validate the hypotheses. The research results indicate a solid inclination for m-commerce among Generation Z. The results are helpful for companies that can shape their marketing strategies to boost their sales using m-commerce channels among the younger population.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing the Adoption of Mobile Technology for Commerce by Generation Z

et al. 2022

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“…The value of leveraging simulated environments to augment training has been explored in various vision tasks, such as object detection, semantic segmentation, and pose estimation [28,32,37,48,57,66]. Synthetic environments have also been applied to vision and language problems, such as embodied agent learning [11,12,16,30,51,55], using platforms such as the Unreal Engine [36,40], and using existing scenes and spaces manually created by specialized designers and content creators [60]. Within the task of VQA, to train and diagnose model performance on compositional questions, synthetic datasets such as CLEVR [26] and CLEVRER [64] have been proposed.…”

Section: Visual Question Answering (Vqa)mentioning

confidence: 99%

SimVQA: Exploring Simulated Environments for Visual Question Answering

Cascante-Bonilla¹,

Wang²,

Wang³

et al. 2022

Preprint

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Existing work on VQA explores data augmentation to achieve better generalization by perturbing images in the dataset or modifying existing questions and answers. While these methods exhibit good performance, the diversity of the questions and answers are constrained by the available images. In this work we explore using synthetic computergenerated data to fully control the visual and language space, allowing us to provide more diverse scenarios. We quantify the effectiveness of leveraging synthetic data for real-world VQA. By exploiting 3D and physics simulation platforms, we provide a pipeline to generate synthetic data to expand and replace type-specific questions and answers without risking exposure of sensitive or personal data that might be present in real images. We offer a comprehensive analysis while expanding existing hyper-realistic datasets to be used for VQA. We also propose Feature Swapping (F-SWAP) -where we randomly switch object-level features during training to make a VQA model more domain invariant. We show that F-SWAP is effective for improving VQA models on real images without compromising on their accuracy to answer existing questions in the dataset.

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“…W HILE recent progress in control and perception has propelled the capabilities of robotic platforms to autonomously operate in unknown and unstructured environments [1]- [4], this has largely focused on pure navigation tasks [5], [6]. In this work, we focus on autonomous mobile manipulation which combines the difficulties of navigating unstructured, human-centered environments with the complexity of jointly controlling the base and arm.…”

Section: Introductionmentioning

confidence: 99%

N$^2$M$^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments

Honerkamp¹,

Welschehold²,

Valada³

2022

Preprint

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Despite its importance in both industrial and service robotics, mobile manipulation remains a significant challenge as it requires a seamless integration of end-effector trajectory generation with navigation skills as well as reasoning over long-horizons. Existing methods struggle to control the large configuration space, and to navigate dynamic and unknown environments. In previous work, we proposed to decompose mobile manipulation tasks into a simplified motion generator for the end-effector in task space and a trained reinforcement learning agent for the mobile base to account for kinematic feasibility of the motion. In this work, we introduce Neural Navigation for Mobile Manipulation (N 2 M 2 ) which extends this decomposition to complex obstacle environments and enables it to tackle a broad range of tasks in real world settings. The resulting approach can perform unseen, long-horizon tasks in unexplored environments while instantly reacting to dynamic obstacles and environmental changes. At the same time, it provides a simple way to define new mobile manipulation tasks. We demonstrate the capabilities of our proposed approach in extensive simulation and real-world experiments on multiple kinematically diverse mobile manipulators. Code and videos are publicly available at http://mobile-rl.cs.uni-freiburg.de.

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A Survey of Embodied AI: From Simulators to Research Tasks

Cited by 134 publications

References 84 publications

Assessing the Adoption of Mobile Technology for Commerce by Generation Z

Assessing the Adoption of Mobile Technology for Commerce by Generation Z

SimVQA: Exploring Simulated Environments for Visual Question Answering

N$^2$M$^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments

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