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Contact-rich manufacturing processes such as surface cleaning, deburring, and polishing require precise force regulation and complex trajectory tracking that are challenging to automate using conventional robot programming methods. Learning from Demonstration …
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
-
Precise autonomous navigation on unstructured planetary surfaces is a critical prerequisite for future exploration missions, particularly in GNSS-denied environments such as the Lunar South Pole or Martian deserts. Traditional Visual Odometry (VO) methods, whi…
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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Data-driven exoskeletons offer the potential for adaptive augmentation of human mobility. Yet their widespread adoption is hindered by labor-intensive biomechanical data collection and manual tuning. Herein, this study presents a highly efficient synthetic dat…
europepmc
2026
置信度 0.80
-
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
-
Exploiting the morphological symmetry of robotic systems, such as humanoid and quadruped robots, is a promising direction for improving robot learning. In deep reinforcement learning (DRL) for robot control, prior studies have leveraged such symmetry to improv…
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
-
Abstract Autonomous systems are ever-performing tasks in complicated and multi-agent conditions in which coordination, scalability, safety, and reliability are key demands. In these environments, non-stationarity of the climate, decentralized information, and …
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
-
Autonomous navigation in underwater environments is challenged by the absence of GPS, degraded visibility, and submerged obstacles. This article investigates these issues using the BlueROV2, an open platform for scientific experimentation. We propose a deep re…
europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
-
Multi-robot coordination under communication constraints is a fundamental challenge in autonomous systems, particularly in underwater environments, where low-bandwidth acoustic links restrict centralized planning and limit decentralized information propagation…
europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2025
置信度 0.80
-
Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these co…
europepmc
2025
置信度 0.80
-
Automation holds the potential to assist surgeons in robotic interventions, shifting their mental work load from visuomotor control to high level decision making. Reinforcement learning has shown promising results in learning complex visuomotor policies, espec…
semanticscholar
P. M. Scheikl, E. Tagliabue, Balázs Gyenes, M. Wagner 等
2023-02-01
置信度 0.74
IEEE Robotics and Automation Letters
-
crossref
Yecheng Ma, William Liang, Hung-Ju Wang, Yuke Zhu 等
2024-09-09T17:25:40Z
置信度 0.70
-
crossref
Andrei Pitkevich, Ilya Makarov
2024-11-05T18:29:39Z
置信度 0.70
-
Designing agile locomotion for quadruped robots often requires extensive expertise and tedious manual tuning. In this paper, we present a system to automate this process by leveraging deep reinforcement learning techniques. Our system can learn quadruped locom…
openalex
Jie Tan, Tingnan Zhang, Erwin Coumans, Atıl Işçen 等
2018-06-26
置信度 0.72
RobotReinforcement learningAgile software developmentComputer scienceProcess (computing)
-
We present a system for non-prehensile manipulation that require a significant number of contact mode transitions and the use of environmental contacts to successfully manipulate an object to a target location. Our method is based on deep reinforcement learnin…
openalex
Minchan Kim, Junhyek Han, Jae‐Hyung Kim, Beomjoon Kim
2023-10-01
置信度 0.72
Prehensile tailComputer scienceObject (grammar)Scheme (mathematics)Artificial intelligence
-
Precise robotic grasping of several novel objects is a huge challenge in manufacturing, automation, and logistics. Most of the current methods for model-free grasping are disadvantaged by the sparse data in grasping datasets and by errors in sensor data and co…
openalex
Lei Zhang, Kaixin Bai, Zhaopeng Chen, Yunlei Shi 等
2022-12-05
置信度 0.72
GRASPArtificial intelligenceComputer scienceGrippersObject (grammar)
-
Reinforcement learning (RL) offers a promising solution for controlling humanoid robots, particularly for bipedal locomotion, by learning adaptive and flexible control strategies. However, direct RL application is hindered by time-consuming trial-and-error pro…
openalex
Donghyeon Kim, Hokyun Lee, Junhyeok Cha, Jaeheung Park
2024-12-12
置信度 0.72
Bridging (networking)Humanoid robotReinforcement learningComputer scienceHuman–computer interaction
-
Inertial measurement units (IMUs) are used for inertial motion tracking (IMT) in a growing number of applications as sensor fusion methods are being advanced in three directions: magnetometer-free IMT methods that eliminate the effect of magnetic disturbances;…
openalex
Simon Bachhuber, Dustin Lehmann, Eva Dorschky, Anne D. Koelewijn 等
2023-08-21
置信度 0.72
KinematicsInertial measurement unitArtificial intelligenceComputer visionComputer science
-
Simulations are widely used in the field of photovoltaic systems as they provide an abundant source of data for the building and training of numerical methods or artificial intelligence techniques. However, the strategies that succeed in simulation may not be …
openalex
Kangshi Wang, Jieming Ma, Ka Lok Man, Kaizhu Huang 等
2021-09-07
置信度 0.72
Photovoltaic systemComputer scienceGaussian processProcess (computing)Gaussian
-
Abstract In the field of robot reinforcement learning (RL), the reality gap has always been a problem that restricts the robustness and generalization of algorithms. We propose Simulation Twin (SimTwin) : a deep RL framework that can help directly transfer the…
openalex
Yuanpei Chen, Chao Zeng, Zhiping Wang, Peng Lu 等
2022-09-07
置信度 0.72
Computer scienceReinforcement learningArtificial intelligenceRobotRobotics
-
The automatic extinguishing strategy (AES) is the core of the decision-making system for intelligent firefighting robots. Inspired by the fire extinguishing action of firefighters, designing a vision-based end-to-end AES aligns with human intuition. However, t…
openalex
Chenyu Chaoxia, Weiwei Shang, Junyi Zhou, Zhiwei Yang 等
2024-11-19
置信度 0.72
FirefightingRobotTransfer (computing)Computer scienceAeronautics
-
Training deep learning models for object detection usually requires a large amount of data, a condition that is not common for most real-world applications, especially in the context of aerial imagery. One possible solution is the use of simulators to generate…
openalex
Augusto José Peterlevitz, Mateus Antonio Chinelatto, Angelo Garangau Menezes, Cezanne Alves Mendes Motta 等
2023-01-01
置信度 0.72
Computer scienceAerial imageSynthetic dataArtificial intelligenceObject detection
-
Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the "reality gap"). Some existing solutions to this sim-to-real problem, such as Grounded Action Transforma…
openalex
Haresh Karnan, Siddharth Desai, Josiah P. Hanna, Garrett Warnell 等
2020-10-24
置信度 0.72
Transformation (genetics)Action (physics)Reinforcement learningComputer scienceTransfer function
-
Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should not require manual tuning or calibration. In the case of a robot operating in a real environment the training process can…
openalex
Homanga Bharadhwaj, Zihan Wang, Yoshua Bengio, Liam Paull
2019-05-01
置信度 0.72
Leverage (statistics)Computer scienceEncoderRobotReinforcement learning
-
In order to train reinforcement learning algorithms, a significant amount of experience is required, so it is common practice to train them in simulation, even when they are intended to be applied in the real world. To improve robustness, camerabased agents ca…
openalex
András Béres, Bálint Gyires-Tóth
2023-01-01
置信度 0.72
Reinforcement learningRobustness (evolution)Computer scienceEncoderArtificial intelligence
-
Data in advanced manufacturing are often sparse and collected from various sensory devices in a heterogeneous and multi-modal fashion. Thus, for such intricate input spaces, learning robust and reliable predictive models for product quality assessments entails…
openalex
Milad Ramezankhani, Mehrtash Harandi, Rudolf Seethaler, Abbas S. Milani
2023-09-22
置信度 0.72
AutoencoderTransfer of learningDeep learningComputer scienceArtificial intelligence
-
Domain randomisation is a very popular method for visual sim-to-real transfer in robotics, due to its simplicity and ability to achieve transfer without any real-world images at all. Nonetheless, a number of design choices must be made to achieve optimal trans…
openalex
Raghad Alghonaim, Edward Johns
2021-05-30
置信度 0.72
BenchmarkingComputer scienceRendering (computer graphics)Artificial intelligenceRobotics
-
Bridging the sim-to-real gap is a core challenge in deploying learned manipulation policies. Sim-to-real learning is attractive because it can replace expensive real robot demonstrations with scalable synthetic data, yet world-action models have not previously…
arxiv
Zixing Wang, Kausik Sivakumar, Jinghuan Shang, Yafei Hu 等
2026-06-30T03:49:31Z
置信度 0.78
cs.RO
-
This chapter addresses the critical challenge of simulation-to-reality (sim-to-real) transfer for deep reinforcement learning (DRL) in bipedal locomotion. After contextualizing the problem within various control architectures, we dissect the ``curse of simulat…
arxiv
Lingfan Bao, Tianhu Peng, Chengxu Zhou
2025-11-09T17:20:04Z
置信度 0.78
cs.RO
-
We study sim-to-real skill transfer and discovery in the context of robotics control using representation learning. We draw inspiration from spectral decomposition of Markov decision processes. The spectral decomposition brings about representation that can li…
arxiv
Haitong Ma, Zhaolin Ren, Bo Dai, Na Li
2024-04-07T19:22:51Z
置信度 0.78
cs.LGcs.RO