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• Real-to-Sim based Hierarchical Knowledge Transfer Learning for Semantic Segmentation in Off-road Environments
  crossref Je-ho Ryu, Yong-hwi Kim, SeungJoo Lee, Mino Kim 等 2026-05-28T06:08:11Z 置信度 0.70
• Illy-Net: A GMM Based Performance Evaluation Framework for Sim-to-Real Transfer of RL Policies
  crossref Subin Im, Jaeseon Lee 2026-01-30T21:00:25Z 置信度 0.70
• Fine Manipulation Using a Tactile Skin: Learning in Simulation and Sim-to-Real Transfer
  crossref Ulf Kasolowsky, Berthold Bäuml 2024-12-25T19:17:39Z 置信度 0.70
• Sim-to-Real Transfer for AUV Fault Control with Deep Reinforcement Learning
  crossref Katell Lagattu, Helene Lechene, Eva Artusi, Gilles Le Chenadec 等 2025-08-11T17:40:45Z 置信度 0.70
• Sim-to-Real Transfer Learning for Humanoid Robot Control: A Mini-Review
  crossref Ziqian Zhao, Mingchuan Zhou, Lingfeng Feng 2026-07-06T19:42:40Z 置信度 0.70
• Sim-to-Real Transfer of Soft Robotic Navigation Strategies That Learns From the Virtual Eye-in-Hand Vision_supp1-3291699.mp4
  crossref Hongliang Ren 2023-07-04T13:46:27Z 置信度 0.70
• Using sim-to-real transfer learning to close gaps between simulation and real environments through reinforcement learning
  crossref Yuto Ushida, Hafiyanda Razan, Shunta Ishizuya, Takuto Sakuma 等 2021-11-29T03:02:40Z 置信度 0.70
• A Capacitive Tactile Sensor Digital Twin for Real-Time Synthetic Data Generation and Sim-to-Real Transfer in NVIDIA Isaac Sim
  With advances in robotic manipulation in recent years, tactile sensing has become increasingly important in scenarios where visual information is unreliable or insufficient. However, the development of learning-based tactile algorithms and policies is limited …
  crossref Berith Atemoztli De la Cruz Sánchez, Jean-Philippe Roberge 2026-08-03T14:16:27Z 置信度 0.70
• A Sim-to-Real Transfer Framework for Enhancing Marine Vehicle Performance in Ocean Environments
  Reinforcement learning (RL) has gained attention for complex decision-making in uncertain environments. However, high costs and risks of real-world experimentation limit its direct application to marine vehicles. This motivates the use of simulation-based trai…
  crossref Ze Zheng, Zihao Wang, Wenbo Xie 2025-11-27T18:54:45Z 置信度 0.70 IEEE/RJS International Conference on Intelligent RObots and Systems
• Domain Adaption as Auxiliary Task for Sim-to-Real Transfer in Vision-based Neuro-Robotic Control
  Architectures for vision-based robot manipulation often utilize separate domain adaption models to allow sim-to-real transfer and an inverse kinematics solver to allow the actual policy to operate in Cartesian space. We present a novel end-to-end visuomotor ar…
  crossref Connor Gäde, Jan-Gerrit Habekost, Stefan Wermter 2024-09-09T17:35:05Z 置信度 0.70 IEEE International Joint Conference on Neural Network
• Model-based Imitation Learning with Sim-to-real Transfer for Mobile Robot Navigation
  crossref Ke Zhang, Enqi Zhao, Zichen Sun, Zheng Fang 等 2026-04-28T19:45:57Z 置信度 0.70
• Two-Policy Cooperative Transfer for Alleviation of Sim-to-Real Gap
  crossref Liangdong Wu, Fangzhou Xiong, Zhiyong Liu 2022-03-01T20:41:38Z 置信度 0.70
• Deep Reinforcement Learning for Sim-to-Real Transfer in a Humanoid Robot Barista
  crossref Ziyuan Wang, Yefan Lin, Leyu Zhao, Jiahang Zhang 等 2025-03-07T18:33:40Z 置信度 0.70
• Sim-to-Real Transfer for Cable Manipulation with Tactile Sensing and Homotopy-Constrained Path Planning
  crossref Haegu Lee, Victor Melbye Staven, Christoffer Sloth 2025-01-20T18:41:59Z 置信度 0.70
• CommonRoad-to-Autoware 2.0: Simplifying Sim-to-Real Transfer of Behavior Planning in Autonomous Driving
  crossref Tobias Mascetta, Gerald Würsching, Sven Pflaumbaum, Matthias Althoff 2026-07-30T19:08:04Z 置信度 0.70
• Physical AI: The Convergence of Foundation Models, Sim-to-Real Transfer, and Embodied Intelligence
  crossref Young-myoung Kang 2026-07-09T05:42:02Z 置信度 0.70
• Deep Reinforcement Learning-Based Autonomous Docking with Multi-Sensor Perception in Sim-to-Real Transfer
  Autonomous docking is a critical capability for enabling fully automated operations in industrial and logistics environments using Autonomous Mobile Robots (AMRs). Traditional rule-based docking approaches often struggle with generalization and robustness in c…
  crossref Yanyan Dai, Kidong Lee 2025-09-05T09:51:21Z 置信度 0.70
• Contact Reduction with Bounded Stiffness for Robust Sim-to-Real Transfer of Robot Assembly
  crossref Nghia Vuong, Quang-Cuong Pham 2023-12-13T14:17:55Z 置信度 0.70
• Optimized System Excitation for Data-Driven Thermal Modeling—A Practical Sim-to-Real Transfer Investigation
  crossref Emebet G. Gedlu, Oliver Wallscheid 2026-06-25T19:47:24Z 置信度 0.70
• Autotuning Bipedal Locomotion MPC with GRFM-Net for Efficient Sim-to-Real Transfer
  crossref Qianzhong Chen, Junheng Li, Sheng Cheng, Naira Hovakimyan 等 2025-11-27T18:54:45Z 置信度 0.70
• Improving Sim-to-Real Transfer in Vision-Based Robot Navigation via Instance-Level GAN-Based Data Augmentation
  crossref Ignacio G. Lopez-Francos, Rory Lipkis, Pavlo G. Vlastos, Adrian Agogino 2025-02-12T09:44:16Z 置信度 0.70
• Sim-to-Real Policy Transfer in Deep Reinforcement Learning
  crossref Yoonsu Jang, Seongwon Yoon, Jongchan Baek, Soohee Han 2024-12-09T18:49:01Z 置信度 0.70
• DrEureka: Language Model Guided Sim-To-Real Transfer
  Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. However, sim-to-real approaches typically rely on manual design and tuning of the task reward function as well as the simulation physics …
  semanticscholar Y. Ma, William Liang, Hung-Ju Wang, Sam Wang 等 2024-06-04 置信度 0.74 Robotics: Science and Systems Conference
• TactGen: Tactile Sensory Data Generation via Zero-Shot Sim-to-Real Transfer
  Recent advances in machine learning have driven a step-change in robot perception with modalities such as vision, where large amounts of training data are readily available or cheap to collect. However, in tactile perception, the relatively high cost of data c…
  semanticscholar Shaohong Zhong, A. Albini, P. Maiolino, Ingmar Posner 2025 置信度 0.74 IEEE Transactions on robotics
• DiSRT-In-Bed: Diffusion-Based Sim-To-Real Transfer Framework for In-Bed Human Mesh Recovery
  In-Bed human mesh recovery can be crucial and enabling for several healthcare applications, including sleep pattern monitoring, rehabilitation support, and pressure ulcer prevention. However, it is difficult to collect large real-world visual datasets in this …
  semanticscholar Jingnan Gao, Ce Zheng, László A. Jeni, Zackory Erickson 2025-04-03 置信度 0.74 Computer Vision and Pattern Recognition
• Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
  Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the behaviours developed by agents in simulation are often specific to the characteris…
  openalex Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel 2018-05-01 置信度 0.72 Task (project management)Computer scienceObject (grammar)Dynamics (music)Bridge (graph theory)
• Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
  Deep reinforcement learning has recently seen huge success across multiple areas in the robotics domain. Owing to the limitations of gathering real-world data, i.e., sample inefficiency and the cost of collecting it, simulation environments are utilized for tr…
  openalex Wenshuai Zhao, Jorge Pena Queralta, Tomi Westerlund 2020-12-01 置信度 0.72 Reinforcement learningComputer scienceArtificial intelligenceTransfer of learningContext (archaeology)
• Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)
  Due to the safety risks and training sample inefficiency, it is often preferred to develop controllers in simulation. However, minor differences between the simulation and the real world can cause a significant sim-to-real gap. This gap can reduce the effectiv…
  openalex Esper, Khalil, Spieck, Jan, Sixdenier, Pierre-Louis, Wildermann, Stefan 等 2017-07-20 置信度 0.72 Computer scienceOptimization algorithmAlgorithmMathematical optimizationMathematics
• Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning
  The growing demand for robots able to act autonomously in complex scenarios has widely accelerated the introduction of Reinforcement Learning (RL) in robots control applications. However, the trial and error intrinsic nature of RL may result in long training t…
  openalex Erica Salvato, Gianfranco Fenu, Eric Medvet, Felice Andrea Pellegrino 2021-01-01 置信度 0.72 RobotReinforcement learningComputer scienceContext (archaeology)Controller (irrigation)
• A digital twin-based sim-to-real transfer for deep reinforcement learning-enabled industrial robot grasping
  openalex Yongkui Liu, Xu He, Ding Liu, Lihui Wang 2022-05-30 置信度 0.72 Reinforcement learningRobotComputer scienceArtificial intelligenceTransfer of learning
• RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer
  The success of deep reinforcement learning (RL) and imitation learning (IL) in vision-based robotic manipulation typically hinges on the expense of large scale data collection. With simulation, data to train a policy can be collected efficiently at scale, but …
  openalex Daniel E. Ho, Kanishka Rao, Zhuo Xu, Eric Jang 等 2021-05-30 置信度 0.72 Computer scienceObject (grammar)Task (project management)Artificial intelligenceDomain (mathematical analysis)
• Auto-Tuned Sim-to-Real Transfer
  Policies trained in simulation often fail when transferred to the real world due to the ‘reality gap’ where the simulator is unable to accurately capture the dynamics and visual properties of the real world. Current approaches to tackle this prob…
  openalex Yuqing Du, Olivia Watkins, Trevor Darrell, Pieter Abbeel 等 2021-05-30 置信度 0.72 Computer scienceDomain (mathematical analysis)Set (abstract data type)Key (lock)Artificial intelligence
• Scalable sim-to-real transfer of soft robot designs
  The manual design of soft robots and their controllers is notoriously challenging, but it could be augmented-or, in some cases, entirely replaced-by automated design tools. Machine learning algorithms can automatically propose, test, and refine designs in simu…
  openalex Sam Kriegman, Amir Mohammadi Nasab, Dylan Shah, Hannah Steele 等 2020-05-01 置信度 0.72 ScalabilityComputer scienceRobotModular designFunction (biology)
• DROPO: Sim-to-real transfer with offline domain randomization
  In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult. …
  openalex Gabriele Tiboni, Karol Arndt, Ville Kyrki 2023-05-12 置信度 0.72 Computer scienceDomain (mathematical analysis)Reinforcement learningRandomizationTransfer of learning
• Understanding Domain Randomization for Sim-to-real Transfer
  Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the real world. Domain randomization -- one of the most popular algorithms for s…
  openalex Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li 等 2021-10-07 置信度 0.72 Bounding overwatchReinforcement learningDomain (mathematical analysis)Computer scienceTransfer (computing)
• Sim-to-Real Transfer with Neural-Augmented Robot Simulation
  International audience
  openalex Florian Golemo, Adrien Ali Taïga, Pierre‐Yves Oudeyer, Aaron Courville 2018-10-29 置信度 0.72 Computer scienceRobotArtificial neural networkMobile robotAugmented reality
• Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning
  This article presents an efficient learning-based method to solve theinverse kinematic(IK) problem on soft robots with highly nonlinear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation fo…
  openalex Guoxin Fang, Yingjun Tian, Zhi-Xin Yang, Jo M. P. Geraedts 等 2022-06-08 置信度 0.72 Jacobian matrix and determinantInverse kinematicsKinematicsDifferentiable functionArtificial neural network
• Adversarial discriminative sim-to-real transfer of visuo-motor policies
  Various approaches have been proposed to learn visuo-motor policies for real-world robotic applications. One solution is first learning in simulation then transferring to the real world. In the transfer, most existing approaches need real-world images with lab…
  openalex Fangyi Zhang, Jürgen Leitner, Zongyuan Ge, Michael Milford 等 2019-08-19 置信度 0.72 Computer scienceArtificial intelligenceDiscriminative modelClutterTask (project management)
• Learning for Attitude Holding of a Robotic Fish: An End-to-End Approach With Sim-to-Real Transfer
  Controlling biomimetic underwater robots in unknown flow fields remains a challenge due to the strong nonlinearity of the fluid. This article investigates the attitude holding task of a robotic fish swimming in reality. Such a typical sensing-based control tas…
  openalex Junzheng Zheng, Tianhao Zhang, Chen Wang, Minglei Xiong 等 2021-08-09 置信度 0.72 Robustness (evolution)RobotReinforcement learningTask (project management)Artificial intelligence
• KOVIS: Keypoint-based Visual Servoing with Zero-Shot Sim-to-Real Transfer for Robotics Manipulation
  We present KOVIS, a novel learning-based, calibration-free visual servoing method for fine robotic manipulation tasks with eye-in-hand stereo camera system. We train the deep neural network only in the simulated environment; and the trained model could be dire…
  openalex En Yen Puang, Keng Peng Tee, Wei Jing 2020-10-24 置信度 0.72 Visual servoingArtificial intelligenceComputer scienceAutoencoderComputer vision
• “Good Robot!”: Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer
  Current Reinforcement Learning (RL) algorithms struggle with long-horizon tasks where time can be wasted exploring dead ends and task progress may be easily reversed. We develop the SPOT framework, which explores within action safety zones, learns about unsafe…
  openalex Andrew Hundt, Benjamin Killeen, Nicholas Greene, Hongtao Wu 等 2020-08-11 置信度 0.72 Reinforcement learningComputer scienceTask (project management)RowVariety (cybernetics)
• Crossing the gap: a deep dive into zero-shot sim-to-real transfer for dynamics
  Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem. A number of solutions have been proposed in recent years, but we have found that many works do not present a thorough evaluation in the real world, or u…
  openalex Eugene Valassakis, Zihan Ding, Edward Johns 2020-07-01 置信度 0.72 Zero (linguistics)Computer scienceTask (project management)Transfer (computing)Range (aeronautics)
• Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory
  Reinforcement Learning (RL) methods have been widely applied for robotic manipulations via sim-to-real transfer, typically with proprioceptive and visual information. However, the incorporation of tactile sensing into RL for contact-rich tasks lacks investigat…
  openalex Zihan Ding, Ya-Yen Tsai, Wang Wei Lee, Bidan Huang 2021-09-27 置信度 0.72 Tactile sensorComputer scienceTask (project management)Reinforcement learningArtificial intelligence
• Bidirectional Sim-to-Real Transfer for GelSight Tactile Sensors With CycleGAN
  GelSight optical tactile sensors have high-resolution and low-cost advantages and have witnessed growing adoption in various contact-rich robotic applications. Sim2Real for GelSight sensors can reduce the time cost and sensor damage during data collection and …
  openalex Weihang Chen, Yuan Xu, Zhenyang Chen, Peiyu Zeng 等 2022-04-13 置信度 0.72 Computer scienceTactile sensorArtificial intelligenceTransmission (telecommunications)Transfer of learning
• Sim-to-Real Transfer for Biped Locomotion
  We present a new approach for transfer of dynamic robot control policies such as biped locomotion from simulation to real hardware. Key to our approach is to perform system identification of the model parameters μ of the hardware (e.g. friction, center-of-mass…
  openalex Wenhao Yu, Visak Kumar, Greg Turk, C. Karen Liu 2019-11-01 置信度 0.72 Task (project management)Identification (biology)Computer scienceKey (lock)Robot
• Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer
  Keypoint detection is an essential building block for many robotic applications like motion capture and pose estimation. Historically, keypoints are detected using uniquely engineered markers such as checkerboards or fiducials. More recently, deep learning met…
  openalex Jingpei Lu, Florian Richter, Michael C. Yip 2022-02-16 置信度 0.72 Artificial intelligenceComputer sciencePoseComputer visionRobot
• Grasp Stability Prediction with Sim-to-Real Transfer from Tactile Sensing
  Robot simulation has been an essential tool for data-driven manipulation tasks. However, most existing simulation frameworks lack either efficient and accurate models of physical interactions with tactile sensors or realistic tactile simulation. This makes the…
  openalex Zilin Si, Zirui Zhu, Arpit Agarwal, Stuart Anderson 等 2022-10-23 置信度 0.72 GRASPRobotComputer scienceContact forceTactile sensor
• Sim-to-Real Transfer for Vision-and-Language Navigation
  We study the challenging problem of releasing a robot in a previously unseen environment, and having it follow unconstrained natural language navigation instructions. Recent work on the task of Vision-and-Language Navigation (VLN) has achieved significant prog…
  openalex Peter Anderson, Ayush Shrivastava, Joanne Truong, Arjun Majumdar 等 2020-11-07 置信度 0.72 Computer scienceArtificial intelligenceDomain (mathematical analysis)RobotTask (project management)
• Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
  Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and alleviate traffic congestion. Recently, promising results have been attained by Reinforcement Learning (RL) methods through trial and erro…
  openalex Longchao Da, Minquan Gao, Hao Mei, Hua Wei 2024-03-24 置信度 0.72 Transfer of learningTransfer (computing)SIGNAL (programming language)Traffic signalComputer science
• Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet
  Abstract Applications of atmospheric pressure plasma jets (APPJs) present challenging feedback control problems due to the complexity of the plasma-substrate interactions. The plasma treatment of complex substrates is particularly sensitive to changes in the p…
  openalex Matthew Witman, Dogan Gidon, David B. Graves, Berend Smit 等 2019-08-19 置信度 0.72 SetpointAtmospheric-pressure plasmaSubstrate (aquarium)PlasmaReinforcement learning
• Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies
  While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly. To increase the practicality of these techniques on real robots, we…
  openalex Fangyi Zhang, Jürgen Leitner, Michael Milford, Peter Corke 2016-10-21 置信度 0.72 Modular designTransfer (computing)Computer scienceNeuroscienceArtificial intelligence
• Unsupervised Adversarial Domain Adaptation for Sim-to-Real Transfer of Tactile Images
  Transferring optical tactile skills learned from simulated environments to the real world benefits many robotic tactile applications, which can reduce the cost of data collection. However, the models purely trained on simulated data are often difficult to gene…
  openalex Xingshuo Jing, Kun Qian, Tudor Jianu, Shan Luo 2023-01-01 置信度 0.72 Artificial intelligenceComputer scienceClassifier (UML)Computer visionTransfer of learning
• Blind Spot Detection for Safe Sim-to-Real Transfer
  Agents trained in simulation may make errors when performing actions in the real world due to mismatches between training and execution environments. These mistakes can be dangerous and difficult for the agent to discover because the agent is unable to predict…
  openalex Ramya Ramakrishnan, Ece Kamar, Debadeepta Dey, Eric Horvitz 等 2020-02-04 置信度 0.72 OracleComputer scienceReinforcement learningMachine learningArtificial intelligence
• Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain Transformer
  Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i.e., sim-to-real transfer). Despite considerable …
  openalex Hang Lai, Weinan Zhang, Xialin He, Yu Chen 等 2023-05-29 置信度 0.72 TerrainComputer scienceTransformerReinforcement learningScalability
• Sim-to-Real Transfer of Accurate Grasping with Eye-In-Hand Observations and Continuous Control
  In the context of deep learning for robotics, we show effective method of training a real robot to grasp a tiny sphere (1.37cm of diameter), with an original combination of system design choices. We decompose the end-to-end system into a vision module and a cl…
  openalex Mengyuan Yan, Iuri Frosio, Stephen Tyree, Jan Kautz 2017-12-08 置信度 0.72 Transfer (computing)Control (management)Computer scienceArtificial intelligenceComputer vision
• Digital Twin (DT)-CycleGAN: Enabling Zero-Shot Sim-to-Real Transfer of Visual Grasping Models
  Deep learning has revolutionized the field of robotics. To deal with the lack of annotated training samples for learning deep models in robotics, Sim-to-Real transfer has been invented and widely used. However, such deep models trained in simulation environmen…
  openalex David Liu, Yuzhong Chen, Zihao Wu 2023-03-08 置信度 0.72 Artificial intelligenceComputer scienceConsistency (knowledge bases)RoboticsAction (physics)
• Zero-Shot Sim-to-Real Transfer of Tactile Control Policies for Aggressive Swing-Up Manipulation
  This letter aims to show that robots equipped with a vision-based tactile sensor can perform dynamic manipulation tasks without prior knowledge of all the physical attributes of the objects to be manipulated. For this purpose, a robotic system is presented tha…
  openalex Thomas Bi, Carmelo Sferrazza, Raffaello D’Andrea 2021-07-01 置信度 0.72 SwingComputer scienceHaptic technologyAdaptation (eye)Control theory (sociology)
• Sim-to-Real Transfer of Soft Robotic Navigation Strategies That Learns From the Virtual Eye-in-Hand Vision
  To steer a soft robot precisely in an unconstructed environment with minimal collision remains an open challenge for soft robots. When the environments are unknown, prior motion planning for navigation may not always be available. This paper presents a novel S…
  openalex Jiewen Lai, Tian-Ao Ren, Wenchao Yue, Shijian Su 等 2023-07-03 置信度 0.72 Computer scienceComputer visionArtificial intelligenceImaging phantomRobot
• Sim-to-real transfer of active suspension control using deep reinforcement learning
  We explore sim-to-real transfer of deep reinforcement learning controllers for a heavy vehicle with active suspensions designed for traversing rough terrain. While related research primarily focuses on lightweight robots with electric motors and fast actuation…
  openalex Viktor Wiberg, Erik Wallin, Arvid Fälldin, Tobias Semberg 等 2024-06-13 置信度 0.72 Computer scienceReinforcement learningTransfer of learningSuspension (topology)Artificial intelligence
• On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer
  We study the choice of action space in robot manipulation learning and sim-to-real transfer. We define metrics that assess the performance, and examine the emerging properties in the different action spaces. We train over 250 reinforcement learning (RL) agents…
  openalex Elie Aljalbout, F. Frank, Maximilian Karl, Patrick van der Smagt 2024-05-08 置信度 0.72 Action (physics)Space (punctuation)Reinforcement learningArtificial intelligenceComputer science
• Calibrating a Soft ERT-Based Tactile Sensor with a Multiphysics Model and Sim-to-real Transfer Learning
  Tactile sensors based on electrical resistance tomography (ERT) have shown many advantages for implementing a soft and scalable whole-body robotic skin; however, calibration is challenging because pressure reconstruction is an ill-posed inverse problem. This p…
  openalex Hyosang Lee, Hyunkyu Park, Gokhan Serhat, Huanbo Sun 等 2020-05-01 置信度 0.72 MultiphysicsCalibrationComputer scienceTactile sensorArtificial intelligence
• Sim-to-Real Transfer of Bolting Tasks with Tight Tolerance
  In this paper, we propose a novel sim-to-real framework to solve bolting tasks with tight tolerance and complex contact geometry which are hard to be modeled. The sim-to-real has desirable features in terms of cost and safety, however, that of the assembly tas…
  openalex Dongwon Son, Hyunsoo Yang, Dongjun Lee 2020-10-24 置信度 0.72 Computer scienceReinforcement learningController (irrigation)Motion planningControl theory (sociology)
• Bayesian Domain Randomization for Sim-to-Real Transfer.
  When learning policies for robot control, the real-world data required is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunately, such polices are often not transferable to the real world due to a mismatch …
  openalex Fabio Muratore, Christian Eilers, Michael Gienger, Jan Peters 2020-03-05 置信度 0.72 Computer scienceDomain (mathematical analysis)Bayesian probabilityContext (archaeology)Black box
• Flexible Robotic Grasping with Sim-to-Real Transfer based Reinforcement Learning.
  Robotic manipulation requires a highly flexible and compliant system. Task-specific heuristics are usually not able to cope with the diversity of the world outside of specific assembly lines and cannot generalize well. Reinforcement learning methods provide a …
  openalex Michel Breyer, Fadri Furrer, Tonči Novković, Roland Siegwart 等 2018-03-13 置信度 0.72 Reinforcement learningComputer scienceHeuristicsLift (data mining)Artificial intelligence
• Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer
  Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system. On the other hand, simulations are a useful alternative as they provide an abundant source of data wit…
  openalex Rae Jeong, Jackie Kay, Francesco Romano, Thomas Lampe 等 2019-10-21 置信度 0.72 Reinforcement learningIntuitionComputer scienceRobotArtificial intelligence
• Markerless Camera-to-Robot Pose Estimation via Self-Supervised Sim-to-Real Transfer
  Solving the camera-to-robot pose is a fundamental requirement for vision-based robot control, and is a process that takes considerable effort and cares to make accurate. Traditional approaches require modification of the robot via markers, and subsequent deep …
  openalex Jingpei Lu, Florian Richter, Michael C. Yip 2023-06-01 置信度 0.72 Artificial intelligenceComputer sciencePoseComputer visionRobot
• How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
  Recently, reinforcement learning (RL) algorithms have demonstrated remarkable success in learning complicated behaviors from minimally processed input. However, most of this success is limited to simulation. While there are promising successes in applying RL a…
  openalex Quan Vuong, Sharad Vikram, Hao Su, Sicun Gao 等 2019-03-28 置信度 0.72 Reinforcement learningInefficiencyComputer scienceDomain (mathematical analysis)Artificial intelligence
• Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer
  The role of deep learning (DL) in robotics has significantly deepened over the last decade. Intelligent robotic systems today are highly connected systems that rely on DL for a variety of perception, control and other tasks. At the same time, autonomous robots…
  openalex Xianjia Yu, Jorge Peña Queralta, Tomi Westerlund 2022-01-01 置信度 0.72 Computer scienceRobotArtificial intelligenceMobile robotRobotics
• Sim-To-Real Transfer Learning Approach for Tracking Multi-DOF Ankle Motions Using Soft Strain Sensors
  A data-driven approach has recently been investigated for identifying human joint angles by means of soft strain sensors because of the corresponding modeling difficulty. However, this approach commonly incurs a high computational burden due to the voluminous …
  openalex Hyunkyu Park, Junhwi Cho, Junghoon Park, Youngjin Na 等 2020-03-09 置信度 0.72 Computer scienceCalibrationWearable computerArtificial neural networkBrace
• Micro-object pose estimation with sim-to-real transfer learning using small dataset
  Abstract Three-dimensional (3D) pose estimation of micro/nano-objects is essential for the implementation of automatic manipulation in micro/nano-robotic systems. However, out-of-plane pose estimation of a micro/nano-object is challenging, since the images are…
  openalex Dandan Zhang, Antoine Barbot, Florent Seichepine, Frank P.-W. Lo 等 2022-04-06 置信度 0.72 Artificial intelligencePoseComputer science3D pose estimationComputer vision
• Sim-to-real transfer of adaptive control parameters for AUV stabilisation under current disturbance
  Learning-based adaptive control methods hold the potential to empower autonomous agents in mitigating the impact of process variations with minimal human intervention. However, their application to autonomous underwater vehicles (AUVs) has been constrained by …
  openalex Thomas Chaffre, Jonathan Wheare, Andrew Lammas, Paulo E. Santos 等 2024-09-10 置信度 0.72 Disturbance (geology)Control theory (sociology)Current (fluid)Control (management)Computer science
• Torque-Based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer
  In this letter, we review the question of which action space is best suited for controlling a real biped robot in combination with Sim2Real training. Position control has been popular as it has been shown to be more sample efficient and intuitive to combine wi…
  openalex Donghyeon Kim, Glen Berseth, Mathew Schwartz, Jaeheung Park 2023-08-11 置信度 0.72 Reinforcement learningTorqueRobotTask (project management)Computer science
• Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks.
  openalex Fangyi Zhang, Jürgen Leitner, Michael Milford, Peter Corke 2017-09-18 置信度 0.72 Modular designClutterRandomizationAdaptation (eye)Modularity (biology)
• Sim-to-Real Transfer for Fixed-Wing Uncrewed Aerial Vehicle: Pitch Control by High-Fidelity Modelling and Domain Randomization
  Deep reinforcement learning has great potential to automatically generate flight controllers for uncrewed aerial vehicles (UAVs), however these controllers often fail to perform as expected in real world environments due to differences between the simulation e…
  openalex Daichi Wada, Sergio A. Araujo-Estrada, Shane P. Windsor 2022-09-09 置信度 0.72 AileronFidelityElevatorController (irrigation)Computer science
• Domain-Invariant Representation Learning for Sim-to-Real Transfer
  Generating large-scale synthetic data in simulation is a feasible alternative to collecting/labelling real data for training vision-based deep learning models, albeit the modelling inaccuracies do not generalize to the physical world. In this paper, we present…
  openalex Ajay Kumar Tanwani 2020-11-15 置信度 0.72 Computer scienceTransfer of learningFeature learningConditional probability distributionArtificial intelligence
• Efficient Sim-to-Real Transfer in Reinforcement Learning Through Domain Randomization and Domain Adaptation
  Reinforcement learning has gained significant interest in modern industries for its advancements in tackling challenging control tasks compared to rule-based programs. However, the robustness aspect of this technique is still under development, limiting its wi…
  openalex Aidar Shakerimov, Tohid Alizadeh, Hüseyin Atakan Varol 2023-01-01 置信度 0.72 Computer scienceReinforcement learningRobustness (evolution)Inverted pendulumArtificial intelligence
• Reinforcement Learning and Sim-to-Real Transfer of Reorientation and Landing Control for Quadruped Robots on Asteroids
  In surface exploration missions, wheeled planetary vehicles have difficulty traveling on asteroids due to their weak gravitational fields. With the rapid development of hardware performance and control methods, quadruped robots have great potential to serve in…
  openalex Ji Qi, Haibo Gao, Huanli Su, Mingying Huo 等 2024-02-27 置信度 0.72 Reinforcement learningAsteroidRobotReinforcementComputer science
• AAV Swarm Cooperative Search Based on Scalable Multiagent Deep Reinforcement Learning With Digital Twin-Enabled Sim-to-Real Transfer
  Cooperative target search (CTS) technology is highly desirable in various multi-autonomous aerial vehicle (AAV) applications. However, searching for unknown targets in a dynamic threatening environment is a challenging problem, especially for AAVs with limited…
  openalex Pan Cao, Lei Lei, Gaoqing Shen, Shengsuo Cai 等 2025-01-15 置信度 0.72 Computer scienceReinforcement learningScalabilitySwarm behaviourTransfer of learning
• Sim-to-Real Transfer of Compliant Bipedal Locomotion on Torque Sensor-Less Gear-Driven Humanoid
  Sim-to-real is a mainstream method to cope with the large number of trials needed by typical deep reinforcement learning methods. However, transferring a policy trained in simulation to actual hardware remains an open challenge due to the reality gap. In parti…
  openalex Shimpei Masuda, Kuniyuki Takahashi 2023-12-12 置信度 0.72 TorqueActuatorComputer scienceHumanoid robotRobot
• Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics
  Learning robot tasks or controllers using deep reinforcement learning has been proven effective in simulations. Learning in simulation has several advantages. For example, one can fully control the simulated environment, including halting motions while perform…
  openalex Jeroen van Baar, Alan Sullivan, Radu Cordorel, Devesh K. Jha 等 2019-05-01 置信度 0.72 RobotTask (project management)Computer scienceTransfer of learningReinforcement learning
• Sim-to-Real Transfer for Optical Tactile Sensing
  Deep learning and reinforcement learning methods have been shown to enable learning of flexible and complex robot controllers. However, the reliance on large amounts of training data often requires data collection to be carried out in simulation, with a number…
  openalex Zihan Ding, Nathan F. Lepora, Edward Johns 2020-05-01 置信度 0.72 Tactile sensorComputer scienceTransfer of learningArtificial neural networkArtificial intelligence
• DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer
  Generating large-scale synthetic data in simulation is a feasible alternative to collecting/labelling real data for training vision-based deep learning models, albeit the modelling inaccuracies do not generalize to the physical world. In this paper, we present…
  openalex Ajay Kumar Tanwani 2020-11-15 置信度 0.72 Invariant (physics)Representation (politics)Computer scienceTransfer (computing)Mathematics
• TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer
  Model-based RL is a promising approach for real-world robotics due to its improved sample efficiency and generalization capabilities compared to model-free RL. However, effective model-based RL solutions for vision-based real-world applications require bridgin…
  openalex Jun Yamada, Marc Rigter, Jack Collins, Ingmar Posner 2024-05-13 置信度 0.72 DistillationTwistComputer scienceTransfer (computing)Parallel computing
• Sim-to-Real Transfer in Reinforcement Learning for Maneuver Control of a Variable-Pitch MAV
  Reinforcement learning (RL) algorithms can enable high-maneuverability in unmanned aerial vehicles (MAVs), but transferring them from simulation to real-world use is challenging. Variable-pitch propeller (VPP) MAVs offer greater agility, yet their complex dyna…
  openalex Zhikun Wang, Shiyu Zhao 2025-04-15 置信度 0.72 Control theory (sociology)Reinforcement learningComputer scienceVariable (mathematics)Control (management)
• Gctf: Real-time CTF determination and correction
  Accurate estimation of the contrast transfer function (CTF) is critical for a near-atomic resolution cryo electron microscopy (cryoEM) reconstruction. Here, a GPU-accelerated computer program, Gctf, for accurate and robust, real-time CTF determination is prese…
  openalex Kai Zhang 2015-11-20 置信度 0.72 Computer science
• Kalman Filter-Based One-Shot Sim-to-Real Transfer Learning
  Deepreinforcement learning algorithms offer a promising method for industrial robots to tackle unstructured and complex scenarios that are difficult to model. However, due to constraints related to equipment lifespan and safety requirements, acquiring a number…
  openalex Qingwei Dong, Peng Zeng, Guangxi Wan, Yunpeng He 等 2023-11-16 置信度 0.72 Computer scienceAdaptabilityDomain (mathematical analysis)Kalman filterRobot
• Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robots
  The light and soft characteristics of Buoyancy Assisted Lightweight Legged Unit (BALLU) robots have a great potential to provide intrinsically safe interactions in environments involving humans, unlike many heavy and rigid robots. However, their unique and sen…
  openalex Nitish Sontakke, Hosik Chae, Sang Joon Lee, Tianle Huang 等 2023-10-01 置信度 0.72 RobotReinforcement learningFidelityPhysics engineComputer science
• Toward Zero-Shot Sim-to-Real Transfer Learning for Pneumatic Soft Robot 3D Proprioceptive Sensing
  Pneumatic soft robots present many advantages in manipulation tasks. Notably, their inherent compliance makes them safe and reliable in unstructured and fragile environments. However, full-body shape sensing for pneumatic soft robots is challenging because of …
  openalex Uksang Yoo, Hanwen Zhao, A. Altamirano, Wenzhen Yuan 等 2023-05-29 置信度 0.72 Computer sciencePoint cloudArtificial intelligenceComputer visionRobot
• On dexterity and dexterous manipulation
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• Dexterous manipulation through rolling
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• A framework for planning dexterous manipulation
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• The JamHand: Dexterous Manipulation with Minimal Actuation.
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• A Preprogrammable Continuum Robot Inspired by Elephant Trunk for Dexterous Manipulation.
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• Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review.
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• Dexterous manipulation is poorer at older ages and is dissociated from decline of hand strength.
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• Sensory and Memory Information in the Control of Dexterous Manipulation
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• Retention and interference of learned dexterous manipulation: interaction between multiple sensorimotor processes.
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• Design and Calibration of a Force/Tactile Sensor for Dexterous Manipulation.
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• Control framework for dexterous manipulation using dynamic visual servoing and tactile sensors' feedback.
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