Learning from demonstration (LfD) has been used to help robots to implement manipulation tasks autonomously, in particular, to learn manipulation behaviors from observing the motion executed by human demonstrators. This paper reviews recent research...
The insufficient generalisation capability of the conventional learning from demonstration (LfD) model necessitates redemonstrations. In addition, retraining the model can overwrite existing knowledge, making it impossible to perform previously acqui...
A major challenge for robots that provide physical assistance is adapting to the needs of different people. To overcome this, personalised assistive models can be created by observing the demonstrations of help provided by an assistant, a setting kno...
Robot grasping constitutes an essential capability in fulfilling the complexities of advanced industrial operations. This field has been extensively investigated to address a range of practical applications. However, the generation of a stable grasp...
This paper provides a structured and practical roadmap for practitioners to integrate learning from demonstration (LfD) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift from mass production t...
Robotic arms have been widely used in various industries and have the advantages of cost savings, high productivity, and efficiency. Although robotic arms are good at increasing efficiency in repetitive tasks, they still need to be re-programmed and...
Collaborative robots face barriers to widespread adoption due to the complexity of programming them to achieve human-like movement. Learning from demonstration (LfD) has emerged as a crucial solution, allowing robots to learn tasks directly from expe...
This paper presents an n-dimensional reduction algorithm for Learning from Demonstration (LfD) for robotic path planning, addressing the complexity of high-dimensional data. The method extends the Douglas–Peucker algorithm by incorporating velo...
Motion primitives are a highly useful and widely employed tool in the field of Learning from Demonstration (LfD). However, obtaining a large number of motion primitives can be a tedious process, as they typically need to be generated individually for...
Paraconsistent Annotated Logic (PAL) is a type of non-classical logic based on concepts that allow, under certain conditions, for one to accept contradictions without invalidating conclusions. The Paraconsistent Artificial Neural Cell of Learning (lP...
Learning from Demonstration (LfD) enables robots to acquire manipulation skills by observing human actions. However, existing methods often face challenges such as high computational cost, limited generalizability, and a loss of key interaction detai...
Unmanned Aerial Vehicle (UAV) operating in complex environments require guaranteed safety mechanisms while maintaining high performance. This study addresses the challenge of ensuring strict flight safety during policy execution by implementing a Con...
The ability of robots to tackle complex non-repetitive tasks will be key in bringing a new level of automation in agricultural applications still involving labor-intensive, menial, and physically demanding activities due to high cognitive requirement...
Teaching robots to learn through human demonstrations is a natural and direct method, and virtual reality technology is an effective way to achieve fast and realistic demonstrations. In this paper, we construct a virtual reality demonstration system...
Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but t...
Dexterous manipulation concerns the control of a robot hand to manipulate an object in a desired manner. While classical dexterous manipulation strategies are based on stable grasping (or force closure), many human-like manipulation tasks do not main...
Most robots are programmed to carry out specific tasks routinely with minor variations. However, more and more applications from SMEs require robots work alongside their counterpart human workers. To smooth the collaboration task flow and improve the...
The strategic transition from fossil energy to renewable energy is an irreversible global trend, but the pace of renewable energy deployment and the path of cost reduction are uncertain. In this paper, a two-factor learning-curve model of wind power...
This article focuses on a sensorial glove prototype capable of acquiring hand motion and estimating its pose. The presented solution features twelve inertial measurement units (IMUs) to track hand orientation. The sensors are attached to a glove to d...
A robot screwing skill learning framework based on teaching–learning is proposed to improve the generalization ability of robots for different scenarios and objects, combined with the experience of a human operation. This framework includes tas...
We introduce a One-Shot Learning system where a robot effectively learns how to manipulate objects by relying solely on the object’s name, a single image, and a visual example of a person picking it up. Once the robot has mastered picking up a...
The construction of New Energy Demonstration Cities (NEDC) represents a crucial policy initiative in advancing China’s energy transition and serves as an institutional innovation to promote inclusive green growth (IGG) at the urban level. Based...
Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities...
In order to enable robots to be more intelligent and flexible, one way is to let robots learn human control strategy from demonstrations. It is a useful methodology, in contrast to traditional preprograming methods, in which robots are required to sh...
In recent years, the fields of one-shot and few-shot object detection and classification have garnered significant attention. However, the rapid adaptation of robots to previously unencountered or novel environments remains a formidable challenge. In...
The penetration of unmanned aerial vehicles (UAVs) is an essential and important link in modern warfare. Enhancing UAV’s ability of autonomous penetration through machine learning has become a research hotspot. However, the current generation o...
Tactile spaces as learning environments influence individuals’ attitudes through social embeddedness or interconnections among people, and physical embodiedness through experiencing surroundings, potentially fostering deep commitments. When on-...
This paper proposes a novel energy management approach (imitation-Q-learning) based on imitation learning (IL) and reinforcement learning (RL). The proposed approach reinforces a decision-making agent based on a modified Q-learning algorithm to mimic...
Human–robot interaction is of the utmost importance as it enables seamless collaboration and communication between humans and robots, leading to enhanced productivity and efficiency. It involves gathering data from humans, transmitting the data...
The construction industry faces significant challenges due to the physically demanding and hazardous nature of tasks such as manual filling of expansion joints with mastic. Automating mastic filling presents additional difficulties due to the variabi...
The “Controlled Hydrogen Fleet and Infrastructure Demonstration and Validation Project,” also known as the National Fuel Cell Electric Vehicle Learning Demonstration, is a U.S. Department of Energy (DOE) project started in 2004 and concluded in late...
This work proposes a Learning by Demonstration framework based on Dynamic Movement Primitives (DMPs) that could be effectively adopted to plan complex activities in robotics such as the ones to be performed in agricultural domains and avoid orientati...
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The purpose of this study was to compare the effects of simulation problem-based learning (S-PBL), a type of learning that reflects various clinical situations, and demonstration-based learning, a conventional type of learning that shows clinical ski...
Neuromorphic computing has shown great advantages towards cognitive tasks with high speed and remarkable energy efficiency. Memristor is considered as one of the most promising candidates for the electronic synapse of the neuromorphic computing syste...
In this paper, a Deep Reinforcement Learning (DRL)-based approach for learning mobile cleaning robot navigation commands that leverage experience from expert demonstrations is presented. First, expert demonstrations of robot motion trajectories in si...
Although robots have been widely used in a variety of fields, the idea of enabling them to perform multiple tasks in the same way that humans do remains a difficulty. To solve this, we investigate the learning from demonstration (LFD) system with our...
Recent studies introduced the importance of using machine learning algorithms in research focused on the identification of antibiotic resistance. In this study, we highlight the importance of building solid machine learning foundations to differentia...
The “Controlled Hydrogen Fleet and Infrastructure Demonstration and Validation Project,” also known as the Fuel Cell Vehicle and Infrastructure Learning Demonstration, is a 5-year U.S. Department of Energy (DOE) project started in 2004. The purpose o...
The National Fuel Cell Electric Vehicle Learning Demonstration is a U.S. Department of Energy (DOE) project that started in 2004. The purpose of this project is to conduct an integrated field validation that simultaneously examines the performance of...
The conventional denoising method in Rayleigh imaging in a general sense requires an additional hardware investment and the use of the underlying physics. This work demonstrates an alternative image denoising reconstruction model based on unsupervise...
Programming by demonstration has shown potential in reducing the technical barriers to teaching complex skills to robots. Dynamic motion primitives (DMPs) are an efficient method of learning trajectories from individual demonstrations using second-or...
Learning from demonstration (LfD) is a practical method for transferring skill knowledge from a human demonstrator to a robot. Several studies have shown the effectiveness of LfD in robotic grasping tasks to improve the success rate of grasping and t...
The person-artifact-task model provided us with a method to consider the practical performance anxiety (PPA) of technical college students who were working on a computer-related task via online learning. This study investigated 474 technical college...
Learning skills from kinesthetic demonstrations is a promising way of minimizing the gap between human manipulation abilities and those of robots. We propose an approach to learn sequential force interaction skills from such demonstrations. The demon...
The quantitative evaluations of mineral resources and delineation of promising areas in survey regions for future mining have attracted many researchers’ interest. Cobalt-Rich manganese crusts (Mn-crusts), as one of the three significant strate...
Autonomy represents one of the most promising directions in the future development of surgical robotics, and Learning from Demonstration (LfD) is a key methodology for advancing technologies in this field. The proposed approach extends the classical...
To provide high-quality location-based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi-photodiodes (multi-PDs) three-dimension...
During farming demonstrations, peer-to-peer learning is known to be more effective than technology transfer when encouraging farmers to consider adopting more sustainable farming practices. Interactive knowledge creation has the potential to create a...
Collaborative robots provide prospective and great solutions to human–robot cooperative tasks. In this paper, we present a comprehensive review for two significant topics in human–robot interaction: robots learning from demonstrations and...
Using expert samples to improve the performance of reinforcement learning (RL) algorithms has become one of the focuses of research nowadays. However, in different application scenarios, it is hard to guarantee both the quantity and quality of expert...
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