Intelligent recognition algorithms deployed on edge devices offer strong real-time processing capabilities and high security for online video image analysis. However, real-time video image recognition remains challeng...
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This paper introduces a method that integrates LiDAR and FastSAM for precise distance measurement, object identification, and segmentation. In the fields of computer vision and intelligent robotics, accurate identific...
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Precipitation images can clearly reflect the rainfall spatio-temporal features and play an important role in hydrological analysis and flood forecasting. However, it is challenging to mine the association response rel...
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Nowadays ubiquitous robots must be adaptive and easy to use. To this end, dynamical system-based imitation learning plays an important role. In fact, it allows to realize stable and complex robotic tasks without expli...
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ISBN:
(纸本)9798350323658
Nowadays ubiquitous robots must be adaptive and easy to use. To this end, dynamical system-based imitation learning plays an important role. In fact, it allows to realize stable and complex robotic tasks without explicitly coding them, thus facilitating the robot use. However, the adaptation capabilities of dynamical systems have not been fully exploited due to the lack of closed-loop implementations making use of visual feedback. In this regard, the integration of visual information allows higher flexibility to cope with environmental changes. This work presents a dynamical system-based imitation learning for visual servoing, based on the large projection task priority formulation. The proposed scheme enables complex and stable visual tasks, as demonstrated by a simulation analysis and experiments with a robotic manipulator.
Changes in topological spatial relations of objects are often strong indicators for state transitions in the underlying processes they are involved in. While various aspects of semantic mapping have been extensively r...
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ISBN:
(纸本)9798350323658
Changes in topological spatial relations of objects are often strong indicators for state transitions in the underlying processes they are involved in. While various aspects of semantic mapping have been extensively researched, the reasoning about the temporal development of spatial relations of instances is often neglected. This paper presents a concept to combine a semantic map with a stream processing framework for live analysis of the spatio-temporal relation of objects, based on the map and information inferred from sensors streams. To demonstrate the functionality of our concept, we implemented a proof-of-concept system to track everyday events in an office environment. The presented application scenario clearly demonstrates the benefits of the proposed architecture for detecting and handling complex spatio-temporal events.
New frontiers in simulation-based teacher training have been unveiled with the advancement of artificial intelligence (AI). Integrating AI into virtual student agents increases the accessibility and affordability of t...
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ISBN:
(纸本)9798400707018
New frontiers in simulation-based teacher training have been unveiled with the advancement of artificial intelligence (AI). Integrating AI into virtual student agents increases the accessibility and affordability of teacher training simulations, but little is known about how preservice teachers interact with AI-powered student agents. This study analyzed the discourse behavior of 15 preservice teachers who undertook simulation-based training with AI-powered student agents. Using a framework of ambitious science teaching, we conducted a patternanalysis of teacher and student talk moves, looking for evidence of academically productive discourse. Comparisons are made with patterns found in real classrooms with professionally trained science teachers. Results indicated that preservice teachers generated academically productive discourse with AI-powered students by using ambitious talk moves. The patternanalysis also revealed coachable moments where preservice teachers succumbed to cycles of unproductive discourse. This study highlights the utility of analyzing classroom discourse to understand human-AI communication in simulation-based teacher training.
The integration of ideas from spiking neural networks and reinforcement learning (RL) algorithms is a promising direction in mobile robotics, as it allows for more energy-efficient solutions capable of handling larger...
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To achieve the reliability and convenience of the anti-corrosion painting inside the pipeline, this paper designs an in-pipe robot driven by Mecanum wheels, which can adapt to pipeline diameters ranging from 400 mm to...
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ISBN:
(纸本)9789819607884;9789819607891
To achieve the reliability and convenience of the anti-corrosion painting inside the pipeline, this paper designs an in-pipe robot driven by Mecanum wheels, which can adapt to pipeline diameters ranging from 400 mm to 800 mm. According to the working conditions inside the pipeline, the overall structure design of the in-pipe robot was carried out based on the motion principle of the Mecanum wheel. The driving velocities of the in-pipe robot were calculated, and the motion of the in-pipe robot was analyzed by ADAMS. The simulation results show that the motion characteristics of the in-pipe robot are consistent with the theoretical analysis, which is stable and reliable.
Robots need to understand articulated objects, such as drawers. The state of articulated structures is commonly estimated using vision, but visual perception is limited when objects are occluded, have few salient feat...
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ISBN:
(纸本)9798350323658
Robots need to understand articulated objects, such as drawers. The state of articulated structures is commonly estimated using vision, but visual perception is limited when objects are occluded, have few salient features, or are not in the camera's field of view. Audio sensing does not face these challenges, since sound propagates in a fundamentally different way than light. Therefore we propose to fuse vision and audio sensing to overcome the challenges faced by vision alone. We estimate motion in several drawers and show that an audiovisual approach estimates drawer motion more reliably than only vision - even in settings where the purely visual approach completely breaks down. Additionally, we perform an in-depth analysis of the regularities that govern how motion in drawers shapes their sound.
Reviewing the previous work of diversity Reinforcement Learning, diversity is often obtained via an augmented loss function, which requires a balance between reward and diversity. Generally, diversity optimization alg...
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ISBN:
(纸本)9798350384581;9798350384574
Reviewing the previous work of diversity Reinforcement Learning, diversity is often obtained via an augmented loss function, which requires a balance between reward and diversity. Generally, diversity optimization algorithms use Multi-armed Bandits algorithms to select the coefficient in the pre-defined space. However, the dynamic distribution of reward signals for MABs or the conflict between quality and diversity limits the performance of these methods. We introduce the Phasic Diversity Optimization (PDO) algorithm, a Population-Based Training framework that separates reward and diversity training into distinct phases instead of optimizing a multi-objective function. In the auxiliary phase, agents with poor performance diversified via determinants will not replace the better agents in the archive. The decoupling of reward and diversity allows us to use an aggressive diversity optimization in the auxiliary phase without performance degradation. Furthermore, we construct a dogfight scenario for aerial agents to demonstrate the practicality of the PDO algorithm. We introduce two implementations of PDO archive and conduct tests in the newly proposed adversarial dogfight and MuJoCo simulations. The results show that our proposed algorithm achieves better performance than baselines.
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