Materials that make robots smart

We posit that embodied artificial intelligence is not only a computational, but also a materials problem. While the importance of material and structural properties in the control loop are well understood, materials can take an active role during control by tight integration of sensors, actuators, computation, and communication. We envision such materials to abstract functionality, therefore making the construction of intelligent robots more straightforward and robust. For example, robots could be made of bones that measure load, muscles that move, skin that provides the robot with information about the kind and location of tactile sensations ranging from pressure to texture and damage, eyes that extract high-level information, and brain material that provides computation in a scalable manner. Such materials will not resemble any existing engineered materials, but rather the heterogeneous components out of which their natural counterparts are made. We describe the state-of-the-art in so-called “robotic materials,” their opportunities for revolutionizing applications ranging from manipulation to autonomous driving by describing two recent robotic materials, a smart skin and a smart tire in more depth, and conclude with open challenges that the robotics community needs to address in collaboration with allies, such as wireless sensor network researchers and polymer scientists.

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Part-Level Graph Convolutional Network for Skeleton-Based Action Recognition

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6759 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11045-11052

Author(s):

Linjiang Huang ◽

Yan Huang ◽

Wanli Ouyang ◽

Liang Wang

Keyword(s):

Action Recognition ◽

State Of The Art ◽

The State ◽

Body Parts ◽

Convolutional Network ◽

Joint Level ◽

Convolutional Networks ◽

Level Information ◽

Benchmark Datasets ◽

High Level

Recently, graph convolutional networks have achieved remarkable performance for skeleton-based action recognition. In this work, we identify a problem posed by the GCNs for skeleton-based action recognition, namely part-level action modeling. To address this problem, a novel Part-Level Graph Convolutional Network (PL-GCN) is proposed to capture part-level information of skeletons. Different from previous methods, the partition of body parts is learnable rather than manually defined. We propose two part-level blocks, namely Part Relation block (PR block) and Part Attention block (PA block), which are achieved by two differentiable operations, namely graph pooling operation and graph unpooling operation. The PR block aims at learning high-level relations between body parts while the PA block aims at highlighting the important body parts in the action. Integrating the original GCN with the two blocks, the PL-GCN can learn both part-level and joint-level information of the action. Extensive experiments on two benchmark datasets show the state-of-the-art performance on skeleton-based action recognition and demonstrate the effectiveness of the proposed method.

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Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

Applied Sciences ◽

10.3390/app11156975 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6975

Author(s):

Tao Zhang ◽

Lun He ◽

Xudong Li ◽

Guoqing Feng

Keyword(s):

Performance Improvement ◽

State Of The Art ◽

Error Rates ◽

Convolutional Network ◽

Convolutional Networks ◽

Sentence Level ◽

End To End ◽

High Level ◽

Improved Accuracy ◽

Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

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A survey: which features are required for dynamic visual simultaneous localization and mapping?

Visual Computing for Industry Biomedicine and Art ◽

10.1186/s42492-021-00086-w ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Zewen Xu ◽

Zheng Rong ◽

Yihong Wu

Keyword(s):

Simultaneous Localization And Mapping ◽

Dynamic Environments ◽

Visual Features ◽

Advantages And Disadvantages ◽

Intelligent Robots ◽

Localization And Mapping ◽

High Level ◽

Static World ◽

Robotic Applications ◽

Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

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Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Sensors ◽

10.3390/s21113783 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3783

Author(s):

Sumbal Malik ◽

Manzoor Ahmed Khan ◽

Hesham El-Sayed

Keyword(s):

Autonomous Vehicles ◽

Communication Technologies ◽

Autonomous Driving ◽

Use Cases ◽

Cooperative Driving ◽

Vehicle To Vehicle ◽

Future Challenges ◽

Vehicle To Infrastructure ◽

High Level ◽

Intersection Management

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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Caged-electron States and Split-electron States in Endohedral Alkali C60

Physical Chemistry Chemical Physics ◽

10.1039/d1cp01341f ◽

2021 ◽

Author(s):

yifan yang ◽

Lorenz S Cederbaum

Keyword(s):

State Of The Art ◽

Electronic States ◽

Electron States ◽

The Common ◽

High Level

The low-lying electronic states of neutral X@C60(X=Li, Na, K, Rb) have been computed and analyzed by employing state-of-the-art high level many-electron methods. Apart from the common charge-separated states, well known...

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Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

IEEE Access ◽

10.1109/access.2021.3073733 ◽

2021 ◽

pp. 1-1

Author(s):

Chih-Hua Tai ◽

Kuo-Hsuan Chung ◽

Ya-Wen Teng ◽

Feng-Ming Shu ◽

Yue-Shan Chang

Keyword(s):

Information Fusion ◽

Mood State ◽

Fusion Technique ◽

Level Information ◽

Information Fusion Technique ◽

High Level

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Transfer Reinforcement Learning for Autonomous Driving

ACM Transactions on Modeling and Computer Simulation ◽

10.1145/3449356 ◽

2021 ◽

Vol 31 (3) ◽

pp. 1-26

Author(s):

Aravind Balakrishnan ◽

Jaeyoung Lee ◽

Ashish Gaurav ◽

Krzysztof Czarnecki ◽

Sean Sedwards

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Transfer Problem ◽

Autonomous Driving ◽

High Fidelity ◽

Rule Based ◽

High Level ◽

Real Vehicle

Reinforcement learning (RL) is an attractive way to implement high-level decision-making policies for autonomous driving, but learning directly from a real vehicle or a high-fidelity simulator is variously infeasible. We therefore consider the problem of transfer reinforcement learning and study how a policy learned in a simple environment using WiseMove can be transferred to our high-fidelity simulator, W ise M ove . WiseMove is a framework to study safety and other aspects of RL for autonomous driving. W ise M ove accurately reproduces the dynamics and software stack of our real vehicle. We find that the accurately modelled perception errors in W ise M ove contribute the most to the transfer problem. These errors, when even naively modelled in WiseMove , provide an RL policy that performs better in W ise M ove than a hand-crafted rule-based policy. Applying domain randomization to the environment in WiseMove yields an even better policy. The final RL policy reduces the failures due to perception errors from 10% to 2.75%. We also observe that the RL policy has significantly less reliance on velocity compared to the rule-based policy, having learned that its measurement is unreliable.

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