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• Robot Policy Evaluation for Sim-to-Real Transfer: A Benchmarking Perspective
  Current vision-based robotics simulation benchmarks have significantly advanced robotic manipulation research. However, robotics is fundamentally a real-world problem, and evaluation for real-world applications has lagged behind in evaluating generalist polici…
  arxiv Xuning Yang, Clemens Eppner, Jonathan Tremblay, Dieter Fox 等 2025-08-14T23:50:41Z 置信度 0.78 cs.RO
• Sim-to-Real gap in RL: Use Case with TIAGo and Isaac Sim/Gym
  This paper explores policy-learning approaches in the context of sim-to-real transfer for robotic manipulation using a TIAGo mobile manipulator, focusing on two state-of-art simulators, Isaac Gym and Isaac Sim, both developed by Nvidia. Control architectures a…
  arxiv Jaume Albardaner, Alberto San Miguel, Néstor García, Magí Dalmau-Moreno 2024-03-11T18:35:32Z 置信度 0.78 cs.RO
• 'Getting out of the closet': Scientific authorship of literary fiction and knowledge transfer
  Some scientists write literary fiction books in their spare time. If these books contain scientific knowledge, literary fiction becomes a mechanism of knowledge transfer. In this case, we could conceptualize literary fiction as non-formal knowledge transfer. W…
  arxiv Joaquín M. Azagra-Caro, Anabel Fernández-Mesa, Nicolás Robinson-García 2018-02-14T10:02:17Z 置信度 0.78 cs.DLphysics.soc-ph
• Heat transfer in buildings : application to air solar heating and Trombe wall design
  The aim of this paper is to briefly recall heat transfer modes and explain their integration within a software dedicated to building simulation (CODYRUN). Detailed elements of the validation of this software are presented and two applications are finally discu…
  arxiv Harry Boyer, Frédéric Miranville, Dimitri Bigot, Stéphane Guichard 等 2012-12-20T08:44:54Z 置信度 0.78 cs.CE
• Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
  The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significan…
  arxiv Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif 2022-07-05T22:07:26Z 置信度 0.78 cs.LGcs.AIcs.CRcs.CVcs.DC
• Flow patterns and heat transfer around six in-line circular cylinders at low Reynolds number
  The flow field and the heat transfer around six in-line iso-thermal circular cylinders has been studied by mean of numerical simulations. Two values of the center to center spacing ($s=3.6d$ and $4d$, where $d$ is the cylinder diameter) at Reynolds number of $…
  arxiv Francesco Fornarelli, Paolo Oresta, Antonio Lippolis 2014-10-22T09:24:06Z 置信度 0.78 physics.flu-dynphysics.comp-ph
• Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
  Imitation learning methods need significant human supervision to learn policies robust to changes in object poses, physical disturbances, and visual distractors. Reinforcement learning, on the other hand, can explore the environment autonomously to learn robus…
  arxiv Marcel Torne, Anthony Simeonov, Zechu Li, April Chan 等 2024-03-06T18:55:36Z 置信度 0.78 cs.ROcs.AIcs.LG
• Planetary Spectrum Generator: an accurate online radiative transfer suite for atmospheres, comets, small bodies and exoplanets
  We have developed an online radiative-transfer suite (https://psg.gsfc.nasa.gov) applicable to a broad range of planetary objects (e.g., planets, moons, comets, asteroids, TNOs, KBOs, exoplanets). The Planetary Spectrum Generator (PSG) can synthesize planetary…
  arxiv Geronimo L. Villanueva, Michael D. Smith, Silvia Protopapa, Sara Faggi 等 2018-03-06T04:05:52Z 置信度 0.78 astro-ph.EP
• Digital Twin Technologies in Predictive Maintenance: Enabling Transferability via Sim-to-Real and Real-to-Sim Transfer
  The advancement of the Internet of Things (IoT) and Artificial Intelligence has catalyzed the evolution of Digital Twins (DTs) from conceptual ideas to more implementable realities. Yet, transitioning from academia to industry is complex due to the absence of …
  arxiv Sizhe Ma, Katherine A. Flanigan, Mario Bergés 2025-05-15T12:56:36Z 置信度 0.78 cs.CEcs.AIcs.CYcs.LG
• Provable Sim-to-real Transfer in Continuous Domain with Partial Observations
  Sim-to-real transfer trains RL agents in the simulated environments and then deploys them in the real world. Sim-to-real transfer has been widely used in practice because it is often cheaper, safer and much faster to collect samples in simulation than in the r…
  arxiv Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang 2022-10-27T16:37:52Z 置信度 0.78 cs.LGcs.AIstat.ML
• eStonefish-Scenes: A Sim-to-Real Validated and Robot-Centric Event-based Optical Flow Dataset for Underwater Vehicles
  Event-based cameras (EBCs) are poised to transform underwater robotics, yet the absence of labelled event-based datasets for underwater environments severely limits progress in tasks such as visual odometry and obstacle avoidance. Real-world event-based optica…
  arxiv Jad Mansour, Sebastian Realpe, Hayat Rajani, Michele Grimaldi 等 2025-05-19T16:26:18Z 置信度 0.78 cs.CV
• Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models
  We propose a method to predict the sim-to-real transfer performance of RL policies. Our transfer metric simplifies the selection of training setups (such as algorithm, hyperparameters, randomizations) and policies in simulation, without the need for extensive …
  arxiv Lei M. Zhang, Matthias Plappert, Wojciech Zaremba 2020-09-27T15:06:54Z 置信度 0.78 cs.LGcs.AIcs.RO
• STARS: Zero-shot Sim-to-Real Transfer for Segmentation of Shipwrecks in Sonar Imagery
  In this paper, we address the problem of sim-to-real transfer for object segmentation when there is no access to real examples of an object of interest during training, i.e. zero-shot sim-to-real transfer for segmentation. We focus on the application of shipwr…
  arxiv Advaith Venkatramanan Sethuraman, Katherine A. Skinner 2023-10-02T21:58:32Z 置信度 0.78 cs.CV
• TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
  Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. The key challenge of this approach is to address simulation-to-reality (sim-to-real) gaps. Previous methods often require domain-specifi…
  arxiv Yunfan Jiang, Chen Wang, Ruohan Zhang, Jiajun Wu 等 2024-05-16T17:59:07Z 置信度 0.78 cs.ROcs.AIcs.LG
• On the Enhancement of Heat Transfer and Reduction of Entropy Generation by Asymmetric Slip in Pressure-Driven Non-Newtonian Microflows
  We study hydrodynamics, heat transfer and entropy generation in pressure-driven microchannel flow of a power-law fluid. Specifically, we address the effect of asymmetry in the slip boundary condition at the channel walls. Constant, uniform but unequal heat flu…
  arxiv Vishal Anand, Ivan C. Christov 2018-03-24T23:06:26Z 置信度 0.78 physics.flu-dyn
• GeCo-SRT: Geometry-aware Continual Adaptation for Robotic Cross-Task Sim-to-Real Transfer
  Bridging the sim-to-real gap is important for applying low-cost simulation data to real-world robotic systems. However, previous methods are severely limited by treating each transfer as an isolated endeavor, demanding repeated, costly tuning and wasting prior…
  arxiv Wenbo Yu, Wenke Xia, Weitao Zhang, Di Hu 2026-02-24T13:15:38Z 置信度 0.78 cs.RO
• SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
  Deploying reinforcement learning (RL) safely in the real world is challenging, as policies trained in simulators must face the inevitable sim-to-real gap. Robust safe RL techniques are provably safe, however difficult to scale, while domain randomization is mo…
  arxiv Yarden As, Chengrui Qu, Benjamin Unger, Dongho Kang 等 2025-09-23T05:03:00Z 置信度 0.78 cs.ROcs.AI
• A Recipe for Efficient Sim-to-Real Transfer in Manipulation with Online Imitation-Pretrained World Models
  We are interested in solving the problem of imitation learning with a limited amount of real-world expert data. Existing offline imitation methods often struggle with poor data coverage and severe performance degradation. We propose a solution that leverages r…
  arxiv Yilin Wang, Shangzhe Li, Haoyi Niu, Zhiao Huang 等 2025-10-02T20:14:39Z 置信度 0.78 cs.RO
• Real-time computation of bio-heat transfer in the fast explicit dynamics finite element algorithm (FED-FEM) framework
  Real-time analysis of bio-heat transfer is very beneficial in improving clinical outcomes of hyperthermia and thermal ablative treatments but challenging to achieve due to large computational costs. This paper presents a fast numerical algorithm well suited fo…
  arxiv Jinao Zhang, Sunita Chauhan 2019-09-08T01:13:51Z 置信度 0.78 cs.CE
• Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation
  Simulation has recently become key for deep reinforcement learning to safely and efficiently acquire general and complex control policies from visual and proprioceptive inputs. Tactile information is not usually considered despite its direct relation to enviro…
  arxiv Alex Church, John Lloyd, Raia Hadsell, Nathan F. Lepora 2021-06-16T13:58:35Z 置信度 0.78 cs.ROcs.AI
• Robotic Sim-to-Real Transfer for Long-Horizon Pick-and-Place Tasks in the Robotic Sim2Real Competition
  This paper presents a fully autonomous robotic system that performs sim-to-real transfer in complex long-horizon tasks involving navigation, recognition, grasping, and stacking in an environment with multiple obstacles. The key feature of the system is the abi…
  arxiv Ming Yang, Hongyu Cao, Lixuan Zhao, Chenrui Zhang 等 2025-03-14T02:16:35Z 置信度 0.78 cs.RO
• Developed and quasi-developed macro-scale heat transfer in micro- and mini-channels with arrays of offset strip fins subject to a uniform heat flux
  In the present work, we examine to what degree the heat transfer can be described as developed on a macro-scale level in typical micro- and mini-channels with offset strip fin arrays subject to a uniform heat flux, considering flow entrance and side-wall effec…
  arxiv Arthur Vangeffelen, Geert Buckinx, Carlo De Servi, Maria Rosaria Vetrano 等 2024-04-26T14:43:06Z 置信度 0.78 physics.flu-dyn
• Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
  Federated Learning (FL) over wireless multi-hop edge computing networks, i.e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm. This paper presents FedEdge simulator, a high-fidelity Linux-based simulator, which enables fast prot…
  arxiv Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan, Minwoo Lee 等 2021-10-18T00:21:07Z 置信度 0.78 cs.NIcs.AI
• On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform
  Digital Twin (DT) technology is expected to play a pivotal role in NextG wireless systems. However, a key challenge remains in the evaluation of data-driven algorithms within DTs, particularly the transfer of learning from simulations to real-world environment…
  arxiv Maxwell McManus, Yuqing Cui, Zhaoxi Zhang, Elizabeth Serena Bentley 等 2024-08-26T17:54:55Z 置信度 0.78 eess.SP
• Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer
  Imitation Learning uses the demonstrations of an expert to uncover the optimal policy and it is suitable for real-world robotics tasks as well. In this case, however, the training of the agent is carried out in a simulation environment due to safety, economic …
  arxiv Zoltán Lőrincz, Márton Szemenyei, Róbert Moni 2022-06-22T01:36:14Z 置信度 0.78 cs.LGcs.CVcs.RO
• Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling
  Reinforcement learning (RL) is playing an increasingly important role in fields such as robotic control and autonomous driving. However, the gap between simulation and the real environment remains a major obstacle to the practical deployment of RL. Agents trai…
  arxiv Zhilin Lin, Shiliang Sun 2025-06-15T06:02:42Z 置信度 0.78 cs.LGcs.AI
• Sim-to-Real Transfer via 3D Feature Fields for Vision-and-Language Navigation
  Vision-and-language navigation (VLN) enables the agent to navigate to a remote location in 3D environments following the natural language instruction. In this field, the agent is usually trained and evaluated in the navigation simulators, lacking effective app…
  arxiv Zihan Wang, Xiangyang Li, Jiahao Yang, Yeqi Liu 等 2024-06-14T07:50:09Z 置信度 0.78 cs.ROcs.CV
• In-Hand Manipulation of Articulated Tools with Dexterous Robot Hands with Sim-to-Real Transfer
  Reinforcement learning (RL) and sim-to-real transfer have advanced rigid-object manipulation. However, policies remain brittle for articulated mechanisms due to contact-rich dynamics that require both stable grasping and simultaneous free in-hand articulation.…
  arxiv Soofiyan Atar, Daniel Huang, Florian Richter, Michael Yip 2025-09-27T02:56:57Z 置信度 0.78 cs.RO
• Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters
  Autonomous navigation in congested maritime environments is a critical capability for a wide range of real-world applications. However, it remains an unresolved challenge due to complex vessel interactions and significant environmental uncertainties. Existing …
  arxiv Xinyu Cui, Xuanfa Jin, Xue Yan, Yongcheng Zeng 等 2026-03-04T13:36:33Z 置信度 0.78 cs.ROcs.AI
• NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
  Recent years have witnessed significant progress in autonomous navigation using reinforcement learning. However, existing approaches largely emphasize reinforcement learning framework design, such as input representations, action spaces, and reward functions, …
  arxiv Zhefan Xu, Hanyu Jin, Kenji Shimada 2026-05-15T02:58:25Z 置信度 0.78 cs.RO
• Imitation learning for sim-to-real transfer of robotic cutting policies based on residual Gaussian process disturbance force model
  Robotic cutting, or milling, plays a significant role in applications such as disassembly, decommissioning, and demolition. Planning and control of cutting in real-world scenarios in uncertain environments is a complex task, with the potential to benefit from …
  arxiv Jamie Hathaway, Rustam Stolkin, Alireza Rastegarpanah 2023-11-07T16:06:05Z 置信度 0.78 cs.RO
• Swim2Real: VLM-Guided System Identification for Sim-to-Real Transfer
  We present Swim2Real, a pipeline that calibrates a 16-parameter robotic fish simulator from swimming videos using vision-language model (VLM) feedback, requiring no hand-designed search stages. Calibrating soft aquatic robots is particularly challenging becaus…
  arxiv Kevin Qiu, Kyle Walker, Mike Y. Michelis, Marek Cygan 等 2026-03-21T14:02:48Z 置信度 0.78 cs.RO
• Difference-Based Relational Learning for Zero-Shot Object-Goal Visual Navigation With Direct Sim-to-Real Transfer
  End-to-end deep reinforcement learning (DRL) for zero-shot object-goal visual navigation remains challenged by the sim-to-real gap, particularly variations in object appearance and restricted camera field-of-view (FoV). This letter proposes a Temporal Differen…
  arxiv Guolei Qi, Feitian Zhang 2026-07-17T05:35:40Z 置信度 0.78 cs.RO
• Can Context Bridge the Reality Gap? Sim-to-Real Transfer of Context-Aware Policies
  Sim-to-real transfer remains a major challenge in reinforcement learning (RL) for robotics, as policies trained in simulation often fail to generalize to the real world due to discrepancies in environment dynamics. Domain Randomization (DR) mitigates this issu…
  arxiv Marco Iannotta, Yuxuan Yang, Johannes A. Stork, Erik Schaffernicht 等 2025-11-06T10:35:21Z 置信度 0.78 cs.RO
• Post-Convergence Sim-to-Real Policy Transfer: A Principled Alternative to Cherry-Picking
  Learning-based approaches, particularly reinforcement learning (RL), have become widely used for developing control policies for autonomous agents, such as locomotion policies for legged robots. RL training typically maximizes a predefined reward (or minimizes…
  arxiv Dylan Khor, Bowen Weng 2025-04-21T19:48:05Z 置信度 0.78 cs.ROcs.LG
• A Schwarz-Christoffel Mapping-based Framework for Sim-to-Real Transfer in Autonomous Robot Operations
  Despite the remarkable acceleration of robotic development through advanced simulation technology, robotic applications are often subject to performance reductions in real-world deployment due to the inherent discrepancy between simulation and reality, often r…
  arxiv Shijie Gao, Nicola Bezzo 2025-03-20T18:37:21Z 置信度 0.78 cs.RO
• 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 …
  arxiv Daniel Ho, Kanishka Rao, Zhuo Xu, Eric Jang 等 2020-11-06T00:30:53Z 置信度 0.78 cs.RO
• SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing
  Linear-deformable manipulation remains challenging due to the complex deformations of objects such as cables and ropes. Prior data-driven approaches, particularly imitation learning, have shown some promise in narrowly defined settings but typically require th…
  arxiv Stone Tao, Jie Xu, Hesam Rabeti, Yashraj Narang 等 2026-07-06T02:48:10Z 置信度 0.78 cs.ROcs.AI
• Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks
  Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems are rarely used in practice due to inherent nonlinearities, friction, and hysteresis, which complicate modelin…
  arxiv Jan Schneider, Mridul Mahajan, Le Chen, Simon Guist 等 2026-04-10T16:52:54Z 置信度 0.78 cs.ROcs.LG
• An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer with Differentiable Simulation
  The sim-to-real gap remains a critical challenge in robotics, hindering the deployment of algorithms trained in simulation to real-world systems. This paper introduces a novel Real-Sim-Real (RSR) loop framework leveraging differentiable simulation to address t…
  arxiv Lu Shi, Yuxuan Xu, Shiyu Wang, Jinhao Huang 等 2025-03-13T07:27:05Z 置信度 0.78 cs.ROcs.LG
• Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Real
  Autonomous Driving requires high levels of coordination and collaboration between agents. Achieving effective coordination in multi-agent systems is a difficult task that remains largely unresolved. Multi-Agent Reinforcement Learning has arisen as a powerful m…
  arxiv Eduardo Candela, Leandro Parada, Luis Marques, Tiberiu-Andrei Georgescu 等 2022-03-22T12:16:57Z 置信度 0.78 cs.RO
• Sim-To-Real Transfer for Miniature Autonomous Car Racing
  Sim-to-real, a term that describes where a model is trained in a simulator then transferred to the real world, is a technique that enables faster deep reinforcement learning (DRL) training. However, differences between the simulator and the real world often ca…
  arxiv Yeong-Jia Roger Chu, Ting-Han Wei, Jin-Bo Huang, Yuan-Hao Chen 等 2020-11-11T08:17:08Z 置信度 0.78 cs.AIcs.LG
• Transfer Learning Toolkit: Primers and Benchmarks
  The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function. It is easy for primary researchers to use this toolkit and to choose proper models…
  arxiv Fuzhen Zhuang, Keyu Duan, Tongjia Guo, Yongchun Zhu 等 2019-11-20T15:30:36Z 置信度 0.78 cs.LGstat.ML
• Sim-to-Real Transfer for Mobile Robots with Reinforcement Learning: from NVIDIA Isaac Sim to Gazebo and Real ROS 2 Robots
  Unprecedented agility and dexterous manipulation have been demonstrated with controllers based on deep reinforcement learning (RL), with a significant impact on legged and humanoid robots. Modern tooling and simulation platforms, such as NVIDIA Isaac Sim, have…
  arxiv Sahar Salimpour, Jorge Peña-Queralta, Diego Paez-Granados, Jukka Heikkonen 等 2025-01-06T10:26:16Z 置信度 0.78 cs.ROcs.LG
• Predicting concentration levels of air pollutants by transfer learning and recurrent neural network
  Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short t…
  arxiv Iat Hang Fong, Tengyue Li, Simon Fong, Raymond K. Wong 等 2025-01-30T23:39:19Z 置信度 0.78 cs.LGcs.NEphysics.ao-ph
• TactSpace: Learning a Physics-enriched Shared Latent Space for Tactile Sim-to-Real Transfer
  Tactile sensing provides direct measurements of contact interactions that are essential for robotic manipulation. However, current simulators lack the fidelity to faithfully model the complex deformation and transduction mechanics of tactile sensors, severely …
  arxiv Arunim Joarder, Arjun Bhardwaj, René Zurbrügg, Mayank Mittal 等 2026-06-17T11:41:27Z 置信度 0.78 cs.RO
• AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer
  Simulation parameter settings such as contact models and object geometry approximations are critical to training robust robotic policies capable of transferring from simulation to real-world deployment. Previous approaches typically handcraft distributions ove…
  arxiv Allen Z. Ren, Hongkai Dai, Benjamin Burchfiel, Anirudha Majumdar 2023-02-09T19:10:57Z 置信度 0.78 cs.ROcs.LGeess.SY
• Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds
  Quadrupeds have gained rapid advancement in their capability of traversing across complex terrains. The adoption of deep Reinforcement Learning (RL), transformers and various knowledge transfer techniques can greatly reduce the sim-to-real gap. However, the cl…
  arxiv Dikai Liu, Tianwei Zhang, Jianxiong Yin, Simon See 2025-03-12T02:15:13Z 置信度 0.78 cs.ROcs.LG
• Zero-shot Sim-to-Real Transfer for Reinforcement Learning-based Visual Servoing of Soft Continuum Arms
  Soft continuum arms (SCAs) soft and deformable nature presents challenges in modeling and control due to their infinite degrees of freedom and non-linear behavior. This work introduces a reinforcement learning (RL)-based framework for visual servoing tasks on …
  arxiv Hsin-Jung Yang, Mahsa Khosravi, Benjamin Walt, Girish Krishnan 等 2025-04-23T17:41:55Z 置信度 0.78 cs.ROeess.SY
• A Scalable Embodied Intelligence Platform for Seamless Real-to-Sim-to-Real Transfer of Household Mobile Manipulation Tasks
  Mobile manipulation is a fundamental capability in embodied intelligence robotics. The growing demand for robust and generalizable manipulation in unstructured household environments has driven rapid progress in embodied intelligence platforms. However, achiev…
  arxiv Kui Yang, Xianlei Long, Haoxuan Li, Yan Ding 等 2026-06-17T03:31:43Z 置信度 0.78 cs.RO
• A Conformal Mapping-based Framework for Robot-to-Robot and Sim-to-Real Transfer Learning
  This paper presents a novel method for transferring motion planning and control policies between a teacher and a learner robot. With this work, we propose to reduce the sim-to-real gap, transfer knowledge designed for a specific system into a different robot, …
  arxiv Shijie Gao, Nicola Bezzo 2021-09-19T20:17:47Z 置信度 0.78 cs.RO
• Nusselt number for steady periodically developed heat transfer in micro- and mini-channels with arrays of offset strip fins subject to a uniform heat flux
  In this work, the Nusselt number is examined for periodically developed heat transfer in micro- and mini-channels with arrays of offset strip fins, subject to a constant heat flux. The Nusselt number is defined on the basis of a heat transfer coefficient which…
  arxiv Arthur Vangeffelen, Geert Buckinx, Carlo De Servi, Maria Rosaria Vetrano 等 2022-04-20T16:34:28Z 置信度 0.78 physics.flu-dynphysics.app-ph
• Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning
  Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and-rescue. While for known environments, offline methods can find provably complet…
  arxiv Arvi Jonnarth, Ola Johansson, Jie Zhao, Michael Felsberg 2024-06-07T13:24:19Z 置信度 0.78 cs.ROcs.LGeess.SY
• Robotic Calibration Based on Haptic Feedback Improves Sim-to-Real Transfer
  When inverse kinematics (IK) is adopted to control robotic arms in manipulation tasks, there is often a discrepancy between the end effector (EE) position of the robot model in the simulator and the physical EE in reality. In most robotic scenarios with sim-to…
  arxiv Juraj Gavura, Michal Vavrecka, Igor Farkas, Connor Gade 2025-07-11T13:20:33Z 置信度 0.78 cs.RO
• Deep Reinforcement Learning for Sim-to-Real Policy Transfer of VTOL-UAVs Offshore Docking Operations
  This paper proposes a novel Reinforcement Learning (RL) approach for sim-to-real policy transfer of Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL-UAV). The proposed approach is designed for VTOL-UAV landing on offshore docking stations in maritim…
  arxiv Ali M. Ali, Aryaman Gupta, Hashim A. Hashim 2024-06-02T23:05:43Z 置信度 0.78 cs.RO
• IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control
  Complex multi-agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based methods. However, learning-based methods often struggle with sim-to-real transfer because they rely on accurat…
  arxiv Chenlong Liu, Zhuohui Zhang, Xinyan Chen, Zhipeng Wang 等 2026-06-25T03:49:25Z 置信度 0.78 cs.ROcs.AI
• Robust Visual Sim-to-Real Transfer for Robotic Manipulation
  Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to real robots. One common approach to bridge…
  arxiv Ricardo Garcia, Robin Strudel, Shizhe Chen, Etienne Arlaud 等 2023-07-28T05:47:24Z 置信度 0.78 cs.ROcs.AIcs.CVcs.LG
• Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control
  Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people. Reinforcement Learning (RL) has shown promising results in optimizing traffic signal control, but current RL-based TSC methods are mainly trained i…
  arxiv Longchao Da, Hao Mei, Romir Sharma, Hua Wei 2023-07-23T17:35:49Z 置信度 0.78 cs.LGcs.AI
• Transfer Learning and Organic Computing for Autonomous Vehicles
  Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These expe…
  arxiv Christofer Fellicious 2018-08-16T12:28:11Z 置信度 0.78 cs.LGcs.ROstat.ML
• Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots
  Legged robots must achieve both robust locomotion and energy efficiency to be practical in real-world environments. Yet controllers trained in simulation often fail to transfer reliably, and most existing approaches neglect actuator-specific energy losses or d…
  arxiv Filip Bjelonic, Fabian Tischhauser, Marco Hutter 2025-09-08T05:32:28Z 置信度 0.78 cs.RO
• Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently
  The idea of style transfer has largely only been explored in image-based tasks, which we attribute in part to the specific nature of loss functions used for style transfer. We propose a general formulation of style transfer as an extension of generative advers…
  arxiv Muthuraman Chidambaram, Yanjun Qi 2017-02-22T11:43:50Z 置信度 0.78 cs.LG
• How to Close Sim-Real Gap? Transfer with Segmentation!
  One fundamental difficulty in robotic learning is the sim-real gap problem. In this work, we propose to use segmentation as the interface between perception and control, as a domain-invariant state representation. We identify two sources of sim-real gap, one i…
  arxiv Mengyuan Yan, Qingyun Sun, Iuri Frosio, Stephen Tyree 等 2020-05-14T23:40:00Z 置信度 0.78 cs.RO
• Simple Control Baselines for Evaluating Transfer Learning
  Transfer learning has witnessed remarkable progress in recent years, for example, with the introduction of augmentation-based contrastive self-supervised learning methods. While a number of large-scale empirical studies on the transfer performance of such mode…
  arxiv Andrei Atanov, Shijian Xu, Onur Beker, Andrei Filatov 等 2022-02-07T17:26:26Z 置信度 0.78 cs.CVcs.LG
• Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin
  We introduce real-is-sim, a new approach to integrating simulation into behavior cloning pipelines. In contrast to real-only methods, which lack the ability to safely test policies before deployment, and sim-to-real methods, which require complex adaptation to…
  arxiv Jad Abou-Chakra, Lingfeng Sun, Krishan Rana, Brandon May 等 2025-04-04T17:05:56Z 置信度 0.78 cs.ROcs.AI
• AptSim2Real: Approximately-Paired Sim-to-Real Image Translation
  Advancements in graphics technology has increased the use of simulated data for training machine learning models. However, the simulated data often differs from real-world data, creating a distribution gap that can decrease the efficacy of models trained on si…
  arxiv Charles Y Zhang, Ashish Shrivastava 2023-03-09T06:18:44Z 置信度 0.78 cs.CVeess.IV
• 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 …
  arxiv Jing Gao, Ce Zheng, Laszlo A. Jeni, Zackory Erickson 2025-04-03T19:57:16Z 置信度 0.78 cs.CV
• End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting
  Whereas reinforcement learning has been applied with success to a range of robotic control problems in complex, uncertain environments, reliance on extensive data - typically sourced from simulation environments - limits real-world deployment due to the domain…
  arxiv Jamie Hathaway, Alireza Rastegarpanah, Rustam Stolkin 2026-01-28T18:45:55Z 置信度 0.78 cs.RO
• How good are variational autoencoders at transfer learning?
  Variational autoencoders (VAEs) are used for transfer learning across various research domains such as music generation or medical image analysis. However, there is no principled way to assess before transfer which components to retrain or whether transfer lea…
  arxiv Lisa Bonheme, Marek Grzes 2023-04-21T06:32:32Z 置信度 0.78 cs.LG
• Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture
  Autonomous surface vessels for floating-waste removal operate under varying hydrodynamics, external disturbances, and challenging water-surface perception. We present a field-validated system that combines camera-based polarimetric perception with a lightweigh…
  arxiv Luis F. W. Batista, Stéphanie Aravecchia, Cédric Pradalier 2026-05-04T12:29:15Z 置信度 0.78 cs.RO
• Reinforcement learning and sim-to-real transfer for adaptive control of AUV
  Apprentissage par renforcement et transfert de la simulation au réel pour la commande adaptative d'un AUV Les pilotes automatiques pour systèmes sans pilote sont généralement conçus sur la base des retours fournis par les capteurs de vitesse et d'orientation. …
  crossref Thomas Chaffre 2026-04-08T07:01:27Z 置信度 0.70
• Miniature Autonomous Vehicle Environment for Sim-to-Real Transfer in Reinforcement Learning
  crossref Stephan Pareigis, Daniel Riege, Tim Tiedemann 2024-11-22T22:41:22Z 置信度 0.70
• Sim-to-Real Transfer For Estimation Over Wireless Networks
  We investigate a simulation to real-world transfer of data-driven models for state estimation using measurements received over a wireless network. Real-world networks, such as vehicular communication systems, face issues like channel impairments and network co…
  crossref Shivangi Agarwal, Adi Asija, Sanjit Kaul, Saket Anand 2023-10-25T08:39:28Z 置信度 0.70
• RSAKD: Decoupling Energy and Comfort for Rapid Sim-to-Real Transfer in HVAC Control
  Reinforcement learning shows promise for Heating, Ventilation, and Air Conditioning (HVAC)optimization, but the sim-to-real gap caused by Domain Distribution Shifts (DDS) severely degrades performance when simulation-trained policies are deployed in real build…
  crossref Yizhen Bao 2026-02-13T12:43:21Z 置信度 0.70
• MKG-RiskNet: Physics-Constrained Sim-to-Real Transfer for Traffic Risk Prediction
  Data-driven traffic risk prediction is constrained by sparse real-world crash data. We propose MKG-RiskNet, a physics-constrained framework trained on simulation data and transferred to real scenarios zero-shot. The method integrates three components: (i) a Ma…
  crossref Fangxia Zhao, Yushuai Qi 2026-03-21T15:42:38Z 置信度 0.70
• Bridging the Reality Gap: Analyzing Sim-to-Real Transfer Techniques for Reinforcement Learning in Humanoid Bipedal Locomotion_supp1-3505784.mp4
  crossref Jaeheung Park 2024-12-13T13:52:27Z 置信度 0.70
• Sim-to-Real Transfer Learning for Real-Time Melt Pool Geometry Prediction in Directed Energy Deposition
  In Directed Energy Deposition (DED), melt pool geometry governs part quality, but its depth lies below the surface and cannot be measured directly in real time, a long-standing barrier to closed-loop control. This paper presents a Sim-to-Real transfer learning…
  crossref Youmna Mahmoud, Nikhil Muralidhar, Chaitanya Krishna Vallabh, Souran Manoochehri 2025-10-18T13:39:02Z 置信度 0.70
• Reinforcement Learning in Robotic Systems: A Review on Sim-to-Real Transfer
  crossref SHAILESH KHAPRE, Rajesh Tiwari, Avantika Singh 2025-08-11T20:44:12Z 置信度 0.70
• Sim-to-Real Transfer with Incremental Environment Complexity for Reinforcement Learning of Depth-based Robot Navigation
  crossref Thomas Chaffre, Julien Moras, Adrien Chan-Hon-Tong, Julien Marzat 2020-07-15T11:36:51Z 置信度 0.70
• TactGen: Tactile Sensory Data Generation via Zero-Shot Sim-to-Real Transfer_supp1-3521967.mp4
  crossref Shaohong Zhong 2024-12-25T14:44:41Z 置信度 0.70
• A Study on Bridging the Gap With Reinforcement Learning: TD3 Optimization for Sim-to-real Transfer from Gazebo to Isaac Sim
  crossref Daeyeol Kang, Jongyoon Park, Pileun Kim 2025-11-11T01:22:09Z 置信度 0.70
• Sim-to-Real Transfer of Environment-Constrained Visuomotor Policy with Pixel-Level Adversarial Domain Adaptation
  crossref Xingshuo Jing, Kun Qian, Boyi Duan, Bo Zhou 2023-08-04T16:19:25Z 置信度 0.70
• Gaussian Process Regression for Sim-to-Real Transfer of Hopping Gaits
  Abstract Simulation-based controllers are relatively easy to build and evaluate, but rarely transfer seamlessly to hardware. This is because of the reality-gap which is the discrepancy between simulations and hardware. Narrowing the reality-gap can speed up th…
  crossref Jeremy Krause, Adel Alaeddini, Pranav A. Bhounsule 2023-11-21T18:31:35Z 置信度 0.70
• Efficient Sim-to-Real Transfer in Reinforcement Learning Through Domain Randomization and Domain Adaptation_supp1-3339568.mp4
  crossref Huseyin Atakan Varol 2023-12-06T14:10:48Z 置信度 0.70
• Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation
  crossref Fengda Zhu, Linchao Zhu, Yi Yang 2020-01-10T02:06:13Z 置信度 0.70
• Depth-PC: Sim-to-Real Transfer for Zero-Shot Visual Servoing via Cross-Modal Fusion_supp1-3615036.mp4
  crossref Chao Ye 2025-09-29T17:55:07Z 置信度 0.70
• Sim-to-Real Image Transfer for RL-Based Bin Picking
  crossref In Jun Park, Hyonyoung Han 2025-01-14T19:40:10Z 置信度 0.70
• Sim-to-Real Transfer of a Deep Reinforcement Learning Approach for Active Stall Protection
  crossref Franziska Hein, Stefan Notter, Walter Fichter 2023-01-21T01:59:16Z 置信度 0.70
• Dynamic Grounding Coordination for Sim-to-Real Transfer in Multi-Agent Traffic Control
  crossref Wang Qiang 2026-05-18T19:44:47Z 置信度 0.70
• Online Behavior-centric Adaptation for Bipedal Robot Sim-to-real Transfer with Unmodeled Dynamics Mismatch_supp1-3648835.mp4
  crossref Yidong Du 2025-12-30T18:39:50Z 置信度 0.70
• Sim-to-Real Transfer in Deep Learning for Agitation Evaluation of Biogas Power Plants
  crossref Andreas Heller, Peter Glösekötter, Lukas Buntkiel, Sebastian Reinecke 等 2023-07-11T01:38:05Z 置信度 0.70
• EASI: Evolutionary Adversarial Simulator Identification for Sim-to-Real Transfer
  Reinforcement Learning (RL) controllers have demonstrated remarkable performance in complex robot control tasks. However, the presence of reality gap often leads to poor performance when deploying policies trained in simulation directly onto real robots. Previ…
  crossref Haoyu Dong, Huiqiao Fu, Wentao Xu, Zhehao Zhou 等 2025-11-03T11:20:56Z 置信度 0.70 Neural Information Processing Systems
• DEAL: Diffusion Evolution Adversarial Learning for Sim-to-Real Transfer
  crossref Wentao Xu, Huiqiao Fu, Haoyu Dong, Zhehao Zhou 等 2026-08-06T14:44:29Z 置信度 0.70
• Pixel2Catch: Multi-Agent Sim-to-Real Transfer for Agile Manipulation with a Single RGB Camera_supp1-3703582.mp4
  crossref Soo-Chul Lim 2026-06-16T19:45:22Z 置信度 0.70
• Real-Time Semantic Segmentation in Natural Environments with SAM-assisted Sim-to-Real Domain Transfer
  crossref Han Wang, Ruben Mascaro, Margarita Chli, Lucas Teixeira 2024-12-25T19:17:39Z 置信度 0.70
• In-Hand Object Classification and Pose Estimation With Sim-to-Real Tactile Transfer for Robotic Manipulation_supp1-3334971.mp4
  crossref Jung Kim 2023-11-21T15:00:37Z 置信度 0.70
• Sim-to-Real Transfer for Robotics Using Model-Free Curriculum Reinforcement Learning
  crossref Mochammad Rizky Diprasetya, Ali Nafih Pullani, Andreas Schwung 2024-06-05T17:38:41Z 置信度 0.70
• Sim-to-Real Transfer for Underwater Wireless Optical Communication Alignment Policy between AUVs
  crossref Yang Weng, Takumi Matsuda, Yuki Sekimori, Joni Pajarinen 等 2022-05-19T20:34:00Z 置信度 0.70
• Online Iterative Learning Enhanced Sim-to-Real Transfer for Efficient Manipulation of Deformable Objects
  crossref Shuai Li 2025-06-23T17:24:33Z 置信度 0.70
• Sicgan-Driven Sim-to-Real Transfer: Zero-Shot Deployment on Robotic Manipulators Through Visual Deception
  crossref Lucía Güitta-López, Lionel Güitta-López, Jaime Boal Martín-Larrauri, Álvaro Jesús López López 2025-02-27T13:36:49Z 置信度 0.70
• A DeepONet-based Transfer Learning Framework for Structural Digital Twins: Bridging the Sim-to-Real Gap with Delta-Correction strategy
  crossref Honghai Chen, Jinglong Chen, Zhenxing Li, Yulang Liu 等 2025-12-12T12:01:25Z 置信度 0.70