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• Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
  We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards. We build upon the Deep Deterministic Policy Gradient (DDPG) algorithm to use demonstrations. Both demonstrations and actual interactions are used…
  openalex Vecerik, Mel, Todd Hester, Jonathan Scholz, Fumin Wang 等 2017-07-27 置信度 0.72 RoboticsReinforcement learningArtificial intelligenceComputer scienceRobot
• Reinforcement learning in robotic applications: a comprehensive survey
  openalex Bharat Singh, Rajesh Kumar, V. P. Singh 2021-04-20 置信度 0.72 Reinforcement learningArtificial intelligenceComputer scienceRoboticsField (mathematics)
• Multi-Robot Learning in a Cooperative Observation Task
  openalex Lynne E. Parker, Claude Touzet 2000-01-01 置信度 0.72 RobotComputer scienceRobot learningTask (project management)Artificial intelligence
• On-Policy Robot Imitation Learning from a Converging Supervisor
  Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor. However, there are many settings where the supervisor may evolve during policy learning, such as a human performing a novel task or an improving algorithmic …
  arxiv Ashwin Balakrishna, Brijen Thananjeyan, Jonathan Lee, Felix Li 等 2019-07-08T07:02:57Z 置信度 0.78 cs.LGcs.AIcs.RO
• Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards
  The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its …
  arxiv Rituraj Kaushik, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret 2018-06-25T09:46:47Z 置信度 0.78 cs.LGcs.AIcs.NEcs.ROstat.ML
• Robot Learning: A Tutorial
  Robot learning is at an inflection point, driven by rapid advancements in machine learning and the growing availability of large-scale robotics data. This shift from classical, model-based methods to data-driven, learning-based paradigms is unlocking unprecede…
  arxiv Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf 等 2025-10-14T11:36:46Z 置信度 0.78 cs.ROcs.LG
• State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey
  With the continuous breakthroughs in core technology, the dawn of large-scale integration of robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on this foundation are undergoing drastic evolution. The fusion of artificial …
  arxiv Bin Wu, C Steve Suh 2024-08-03T21:22:08Z 置信度 0.78 cs.ROcs.AI
• Untangling Dense Knots by Learning Task-Relevant Keypoints
  Untangling ropes, wires, and cables is a challenging task for robots due to the high-dimensional configuration space, visual homogeneity, self-occlusions, and complex dynamics. We consider dense (tight) knots that lack space between self-intersections and pres…
  arxiv Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan, Jeffrey Ichnowski 等 2020-11-10T09:29:01Z 置信度 0.78 cs.ROcs.AIcs.LG
• Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy
  An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existing safety methods often neglect the robot's ability to learn and adapt at runtime…
  arxiv Haimin Hu, Zixu Zhang, Kensuke Nakamura, Andrea Bajcsy 等 2023-09-03T20:34:01Z 置信度 0.78 cs.ROcs.AIcs.LGeess.SY
• Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation
  We present a method for Temporal Difference (TD) learning that addresses several challenges faced by robots learning to navigate in a marine environment. For improved data efficiency, our method reduces TD updates to Gaussian Process regression. To make predic…
  arxiv John Martin, Jinkun Wang, Brendan Englot 2018-10-02T13:04:47Z 置信度 0.78 cs.LGstat.ML
• RoboNet: Large-Scale Multi-Robot Learning
  Robot learning has emerged as a promising tool for taming the complexity and diversity of the real world. Methods based on high-capacity models, such as deep networks, hold the promise of providing effective generalization to a wide range of open-world environ…
  arxiv Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair 等 2019-10-24T15:20:03Z 置信度 0.78 cs.ROcs.CVcs.LG
• MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
  Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents. However, the demonstrations can be extremely costly and time-consuming to collect. We introduce MimicGen, a system for automat…
  arxiv Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola 等 2023-10-26T17:17:31Z 置信度 0.78 cs.ROcs.AIcs.CVcs.LG
• ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
  ROBEL is an open-source platform of cost-effective robots designed for reinforcement learning in the real world. ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-fingered hand ro…
  arxiv Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte 等 2019-09-25T17:38:52Z 置信度 0.78 cs.ROcs.LGstat.ML
• Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability
  Many important robotics problems are partially observable in the sense that a single visual or force-feedback measurement is insufficient to reconstruct the state. Standard approaches involve learning a policy over beliefs or observation-action histories. Howe…
  arxiv Hai Nguyen, Brett Daley, Xinchao Song, Christopher Amato 等 2020-10-19T02:02:21Z 置信度 0.78 cs.ROcs.AIcs.LG
• Emotion in Reinforcement Learning Agents and Robots: A Survey
  This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by inf…
  arxiv Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker 2017-05-15T11:49:56Z 置信度 0.78 cs.LGcs.AIcs.HCcs.ROstat.ML
• panda-gym: Open-source goal-conditioned environments for robotic learning
  This paper presents panda-gym, a set of Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym. Five tasks are included: reach, push, slide, pick & place and stack. They all follow a Multi-Goal RL framework, al…
  arxiv Quentin Gallouédec, Nicolas Cazin, Emmanuel Dellandréa, Liming Chen 2021-06-25T15:13:36Z 置信度 0.78 cs.LG
• Auditing Robot Learning for Safety and Compliance during Deployment
  Robots of the future are going to exhibit increasingly human-like and super-human intelligence in a myriad of different tasks. They are also likely going to fail and be incompliant with human preferences in increasingly subtle ways. Towards the goal of achievi…
  arxiv Homanga Bharadhwaj 2021-10-12T02:40:11Z 置信度 0.78 cs.ROcs.AIcs.LG
• Contextual Imagined Goals for Self-Supervised Robotic Learning
  While reinforcement learning provides an appealing formalism for learning individual skills, a general-purpose robotic system must be able to master an extensive repertoire of behaviors. Instead of learning a large collection of skills individually, can we ins…
  arxiv Ashvin Nair, Shikhar Bahl, Alexander Khazatsky, Vitchyr Pong 等 2019-10-23T18:00:18Z 置信度 0.78 cs.ROcs.CVcs.LG
• Benchmarking Reinforcement Learning Algorithms on Real-World Robots
  Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research community is now able to reproduce, analyze and build quickly on these results due to o…
  arxiv A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma 等 2018-09-20T16:46:04Z 置信度 0.78 cs.LGcs.AIcs.ROstat.ML
• Robot Perception enables Complex Navigation Behavior via Self-Supervised Learning
  Learning visuomotor control policies in robotic systems is a fundamental problem when aiming for long-term behavioral autonomy. Recent supervised-learning-based vision and motion perception systems, however, are often separately built with limited capabilities…
  arxiv Marvin Chancán, Michael Milford 2020-06-16T07:45:47Z 置信度 0.78 cs.ROcs.LG
• Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning
  Practical Imitation Learning (IL) systems rely on large human demonstration datasets for successful policy learning. However, challenges lie in maintaining the quality of collected data and addressing the suboptimal nature of some demonstrations, which can com…
  arxiv Sachit Kuhar, Shuo Cheng, Shivang Chopra, Matthew Bronars 等 2023-10-22T06:08:55Z 置信度 0.78 cs.ROcs.AI
• Control with Patterns: A D-learning Method
  Learning-based control policies are widely used in various tasks in the field of robotics and control. However, formal (Lyapunov) stability guarantees for learning-based controllers with nonlinear dynamical systems are difficult to obtain. We propose a novel c…
  arxiv Quan Quan, Kai-Yuan Cai, Chenyu Wang 2022-06-08T11:14:13Z 置信度 0.78 cs.RO
• Understanding Teacher Gaze Patterns for Robot Learning
  Human gaze is known to be a strong indicator of underlying human intentions and goals during manipulation tasks. This work studies gaze patterns of human teachers demonstrating tasks to robots and proposes ways in which such patterns can be used to enhance rob…
  arxiv Akanksha Saran, Elaine Schaertl Short, Andrea Thomaz, Scott Niekum 2019-07-16T18:14:38Z 置信度 0.78 cs.RO
• Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics
  Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning. Skills are typically extracted from expert demonstrations and are embedded into a latent space from which they can be sample…
  arxiv Krishan Rana, Ming Xu, Brendan Tidd, Michael Milford 等 2022-11-04T02:42:17Z 置信度 0.78 cs.ROcs.AIcs.LG
• Learning Robot Manipulation from Cross-Morphology Demonstration
  Some Learning from Demonstrations (LfD) methods handle small mismatches in the action spaces of the teacher and student. Here we address the case where the teacher's morphology is substantially different from that of the student. Our framework, Morphological A…
  arxiv Gautam Salhotra, I-Chun Arthur Liu, Gaurav Sukhatme 2023-04-07T20:21:47Z 置信度 0.78 cs.ROcs.LG
• Learning Model Preconditions for Planning with Multiple Models
  Different models can provide differing levels of fidelity when a robot is planning. Analytical models are often fast to evaluate but only work in limited ranges of conditions. Meanwhile, physics simulators are effective at modeling complex interactions between…
  arxiv Alex LaGrassa, Oliver Kroemer 2022-06-11T17:31:07Z 置信度 0.78 cs.RO
• Smooth Exploration for Robotic Reinforcement Learning
  Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very successful in simulation -- leads to jerky motion patterns on real robots. Cons…
  arxiv Antonin Raffin, Jens Kober, Freek Stulp 2020-05-12T12:28:25Z 置信度 0.78 cs.LGcs.ROstat.ML
• Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
  Many real-world control problems involve both discrete decision variables - such as the choice of control modes, gear switching or digital outputs - as well as continuous decision variables - such as velocity setpoints, control gains or analogue outputs. Howev…
  arxiv Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe 等 2020-01-02T14:19:33Z 置信度 0.78 cs.LGcs.ROstat.ML
• Learning Visualization Policies of Augmented Reality for Human-Robot Collaboration
  In human-robot collaboration domains, augmented reality (AR) technologies have enabled people to visualize the state of robots. Current AR-based visualization policies are designed manually, which requires a lot of human efforts and domain knowledge. When too …
  arxiv Kishan Chandan, Jack Albertson, Shiqi Zhang 2022-11-13T22:03:20Z 置信度 0.78 cs.ROcs.HCcs.LG
• Robot Learning from Human Videos: A Survey
  A critical bottleneck hindering further advancement in embodied AI and robotics is the challenge of scaling robot data. To address this, the field of learning robot manipulation skills from human video data has attracted rapidly growing attention in recent yea…
  arxiv Junyi Ma, Erhang Zhang, Haoran Yang, Ditao Li 等 2026-04-30T09:11:25Z 置信度 0.78 cs.ROcs.CV
• The Sound of Simulation: Learning Multimodal Sim-to-Real Robot Policies with Generative Audio
  Robots must integrate multiple sensory modalities to act effectively in the real world. Yet, learning such multimodal policies at scale remains challenging. Simulation offers a viable solution, but while vision has benefited from high-fidelity simulators, othe…
  arxiv Renhao Wang, Haoran Geng, Tingle Li, Feishi Wang 等 2025-07-03T17:59:58Z 置信度 0.78 cs.ROcs.CV
• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation
  We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. In order to accomplish this, humans need easy and effective ways of specifying tasks to the robot. Goal images are one popular form o…
  arxiv Suraj Nair, Eric Mitchell, Kevin Chen, Brian Ichter 等 2021-09-02T17:42:13Z 置信度 0.78 cs.ROcs.AIcs.LG
• Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning
  Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots. However, effectively utilizing predictive models for control, especially with raw image inputs, poses a number of major challenges. How …
  arxiv Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine 等 2018-10-06T19:51:46Z 置信度 0.78 cs.ROcs.AIcs.CV
• Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions
  We present a robotic setup for real-world testing and evaluation of human-robot and human-human collaborative learning. Leveraging the sample-efficiency of the Soft Actor-Critic algorithm, we have implemented a robotic platform able to learn a non-trivial coll…
  arxiv Jonas Tjomsland, Ali Shafti, A. Aldo Faisal 2019-12-02T12:07:23Z 置信度 0.78 cs.ROcs.AIcs.LG
• ClutterGen: A Cluttered Scene Generator for Robot Learning
  We introduce ClutterGen, a physically compliant simulation scene generator capable of producing highly diverse, cluttered, and stable scenes for robot learning. Generating such scenes is challenging as each object must adhere to physical laws like gravity and …
  arxiv Yinsen Jia, Boyuan Chen 2024-07-07T16:03:36Z 置信度 0.78 cs.RO
• STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience
  Terrain awareness, i.e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigation. Current approaches that provide robots with this awareness either rely on lab…
  arxiv Haresh Karnan, Elvin Yang, Daniel Farkash, Garrett Warnell 等 2023-09-26T22:55:32Z 置信度 0.78 cs.ROcs.AIcs.CVcs.LG
• Learning from My Partner's Actions: Roles in Decentralized Robot Teams
  When teams of robots collaborate to complete a task, communication is often necessary. Like humans, robot teammates should implicitly communicate through their actions: but interpreting our partner's actions is typically difficult, since a given action may hav…
  arxiv Dylan P. Losey, Mengxi Li, Jeannette Bohg, Dorsa Sadigh 2019-10-16T21:07:39Z 置信度 0.78 cs.ROcs.AI
• RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation
  Imitation Learning has empowered recent advances in learning robotic manipulation tasks by addressing shortcomings of Reinforcement Learning such as exploration and reward specification. However, research in this area has been limited to modest-sized datasets …
  arxiv Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher 等 2018-11-07T08:01:21Z 置信度 0.78 cs.ROcs.AIcs.LG
• Learning under Misspecified Objective Spaces
  Learning robot objective functions from human input has become increasingly important, but state-of-the-art techniques assume that the human's desired objective lies within the robot's hypothesis space. When this is not true, even methods that keep track of un…
  arxiv Andreea Bobu, Andrea Bajcsy, Jaime F. Fisac, Anca D. Dragan 2018-10-11T17:58:27Z 置信度 0.78 cs.LGcs.AIcs.HCcs.ROstat.ML
• ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data
  Audio signals provide rich information for the robot interaction and object properties through contact. This information can surprisingly ease the learning of contact-rich robot manipulation skills, especially when the visual information alone is ambiguous or …
  arxiv Zeyi Liu, Cheng Chi, Eric Cousineau, Naveen Kuppuswamy 等 2024-06-27T18:06:38Z 置信度 0.78 cs.ROcs.AIcs.CVcs.SDeess.AS
• Autonomous Improvement of Instruction Following Skills via Foundation Models
  Intelligent instruction-following robots capable of improving from autonomously collected experience have the potential to transform robot learning: instead of collecting costly teleoperated demonstration data, large-scale deployment of fleets of robots can qu…
  arxiv Zhiyuan Zhou, Pranav Atreya, Abraham Lee, Homer Walke 等 2024-07-30T08:26:44Z 置信度 0.78 cs.ROcs.AI
• Learning Inertial Odometry for Dynamic Legged Robot State Estimation
  This paper introduces a novel proprioceptive state estimator for legged robots based on a learned displacement measurement from IMU data. Recent research in pedestrian tracking has shown that motion can be inferred from inertial data using convolutional neural…
  arxiv Russell Buchanan, Marco Camurri, Frank Dellaert, Maurice Fallon 2021-11-01T09:37:34Z 置信度 0.78 cs.RO
• Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation
  A general-purpose intelligent robot must be able to learn autonomously and be able to accomplish multiple tasks in order to be deployed in the real world. However, standard reinforcement learning approaches learn separate task-specific policies and assume the …
  arxiv Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine 2018-10-16T17:49:43Z 置信度 0.78 cs.ROcs.AIcs.LG
• SAVOR: Skill Affordance Learning from Visuo-Haptic Perception for Robot-Assisted Bite Acquisition
  Robot-assisted feeding requires reliable bite acquisition, a challenging task due to the complex interactions between utensils and food with diverse physical properties. These interactions are further complicated by the temporal variability of food properties-…
  arxiv Zhanxin Wu, Bo Ai, Tom Silver, Tapomayukh Bhattacharjee 2025-06-03T01:14:45Z 置信度 0.78 cs.RO
• Active learning for data streams: a survey
  Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recen…
  arxiv Davide Cacciarelli, Murat Kulahci 2023-02-17T14:24:13Z 置信度 0.78 stat.MLcs.LGstat.ME
• Mind Your Manners! A Dataset and A Continual Learning Approach for Assessing Social Appropriateness of Robot Actions
  To date, endowing robots with an ability to assess social appropriateness of their actions has not been possible. This has been mainly due to (i) the lack of relevant and labelled data, and (ii) the lack of formulations of this as a lifelong learning problem. …
  arxiv Jonas Tjomsland, Sinan Kalkan, Hatice Gunes 2020-07-24T12:56:33Z 置信度 0.78 cs.ROcs.HC
• Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration
  In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn is used to synthesize specific motion controllers at run-time.…
  arxiv Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Chitta Baral 等 2019-11-26T18:27:51Z 置信度 0.78 cs.ROcs.CLcs.CVcs.LG
• Toward Aligning Human and Robot Actions via Multi-Modal Demonstration Learning
  Understanding action correspondence between humans and robots is essential for evaluating alignment in decision-making, particularly in human-robot collaboration and imitation learning within unstructured environments. We propose a multimodal demonstration lea…
  arxiv Azizul Zahid, Jie Fan, Farong Wang, Ashton Dy 等 2025-04-14T21:14:51Z 置信度 0.78 cs.ROcs.AIcs.CV
• ARCH: Hierarchical Hybrid Learning for Long-Horizon Contact-Rich Robotic Assembly
  Generalizable long-horizon robotic assembly requires reasoning at multiple levels of abstraction. While end-to-end imitation learning (IL) is a promising approach, it typically requires large amounts of expert demonstration data and often struggles to achieve …
  arxiv Jiankai Sun, Aidan Curtis, Yang You, Yan Xu 等 2024-09-24T20:42:42Z 置信度 0.78 cs.RO
• On the Similarities of Embeddings in Contrastive Learning
  Contrastive learning operates on a simple yet effective principle: Embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. In this paper, we propose a unified framework for understanding contrastive learning through th…
  arxiv Chungpa Lee, Sehee Lim, Kibok Lee, Jy-yong Sohn 2025-06-11T14:21:05Z 置信度 0.78 cs.LGstat.ML
• TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning
  Exploiting the promise of recent advances in imitation learning for mobile manipulation will require the collection of large numbers of human-guided demonstrations. This paper proposes an open-source design for an inexpensive, robust, and flexible mobile manip…
  arxiv Jimmy Wu, William Chong, Robert Holmberg, Aaditya Prasad 等 2024-12-11T18:54:22Z 置信度 0.78 cs.ROcs.AIcs.CVcs.LG
• KDPE: A Kernel Density Estimation Strategy for Diffusion Policy Trajectory Selection
  Learning robot policies that capture multimodality in the training data has been a long-standing open challenge for behavior cloning. Recent approaches tackle the problem by modeling the conditional action distribution with generative models. One of these appr…
  arxiv Andrea Rosasco, Federico Ceola, Giulia Pasquale, Lorenzo Natale 2025-08-14T10:25:39Z 置信度 0.78 cs.RO
• iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
  Recent research in embodied AI has been boosted by the use of simulation environments to develop and train robot learning approaches. However, the use of simulation has skewed the attention to tasks that only require what robotics simulators can simulate: moti…
  arxiv Chengshu Li, Fei Xia, Roberto Martín-Martín, Michael Lingelbach 等 2021-08-06T18:41:39Z 置信度 0.78 cs.ROcs.AIcs.CVcs.LG
• The RobotSlang Benchmark: Dialog-guided Robot Localization and Navigation
  Autonomous robot systems for applications from search and rescue to assistive guidance should be able to engage in natural language dialog with people. To study such cooperative communication, we introduce Robot Simultaneous Localization and Mapping with Natur…
  arxiv Shurjo Banerjee, Jesse Thomason, Jason J. Corso 2020-10-23T19:58:17Z 置信度 0.78 cs.ROcs.AIcs.CLcs.CV
• Human-Guided Reinforcement Learning With Sim-to-Real Transfer for Autonomous Navigation.
  europepmc 2023 置信度 0.80
• A Survey of Sim-to-Real Transfer Techniques Applied to Reinforcement Learning for Bioinspired Robots.
  europepmc 2023 置信度 0.80
• Sim-to-real transfer and reality gap modeling in model predictive control for autonomous driving
  The main challenge for the adoption of autonomous driving is to ensure an adequate level of safety. Considering the almost infinite variability of possible scenarios that autonomous vehicles would have to face, the use of autonomous driving simulators is becom…
  semanticscholar I. G. Daza, R. Izquierdo, L. Martínez, Ola Benderius 等 2022-10-01 置信度 0.74 Applied intelligence (Boston)
• Soft Robots Learn to Crawl: Jointly Optimizing Design and Control with Sim-to-Real Transfer
  This work provides a complete framework for the simulation, co-optimization, and sim-to-real transfer of the design and control of soft legged robots. The compliance of soft robots provides a form of"mechanical intelligence"-- the ability to passively exhibit …
  semanticscholar Charles B. Schaff, Audrey Sedal, Matthew R. Walter 2022-02-09 置信度 0.74 Robotics: Science and Systems Conference
• Learning Soft Millirobot Multimodal Locomotion with Sim-to-Real Transfer.
  europepmc 2024 置信度 0.80
• PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing
  In autonomous and mobile robotics, one of the main challenges is the robust on-the-fly perception of the environment, which is often unknown and dynamic, like in autonomous drone racing. In this work, we propose a novel deep neural network-based perception met…
  semanticscholar H. Pham, Andriy Sarabakha, Mykola Odnoshyvkin, Erdal Kayacan 2022-07-28 置信度 0.74 IEEE Robotics and Automation Letters
• Sim-to-Real Transfer of Robotic Assembly with Visual Inputs Using CycleGAN and Force Control
  Recently, deep reinforcement learning (RL) has shown some impressive successes in robotic manipulation applications. However, training robots in the real world is nontrivial owing to sample efficiency and safety concerns. Sim-to-real transfer is proposed to ad…
  semanticscholar C. Yuan, Yunlei Shi, Qian Feng, Chunyang Chang 等 2022-08-30 置信度 0.74 IEEE International Conference on Robotics and Biomimetics
• Sim-to-Real Transfer in Robotics: Addressing the Gap between Simulation and Real-World Performance
  Sim-to-real transfer in robotics remains a significant challenge due to the inherent differences between simulated environments and real-world conditions, often leading to performance degradation when models are deployed in practical applications. This paper r…
  semanticscholar N. Chukwurah, A. Adebayo, O. Ajayi 2024 置信度 0.74 Journal of Frontiers in Multidisciplinary Research
• 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…
  semanticscholar Ricardo Garcia Pinel, Robin Strudel, Shizhe Chen, Etienne Arlaud 等 2023-07-28 置信度 0.74 IEEE/RJS International Conference on Intelligent RObots and Systems
• Reinforcement learning in robotic systems : A review on sim-to-real transfer
  semanticscholar R. Tiwari, S. Khapre, Avantika Singh 2026-01-01 置信度 0.74 Robotics Auton. Syst.
• Sim-to-Real Transfer for a Robotics Task: Challenges and Lessons Learned
  Reinforcement learning has been successfully applied to many robotic and non-robotic tasks in recent years. However, most of these developments have focused solely on simulated environments, eliminating safety concerns associated with a real environment and al…
  semanticscholar J. Rothert, Sebastian Lang, M. Seidel, Magnus Hanses 2024-09-10 置信度 0.74 IEEE International Conference on Emerging Technologies and Factory Automation
• One-shot sim-to-real transfer policy for robotic assembly via reinforcement learning with visual demonstration
  Abstract Reinforcement learning (RL) has been successfully applied to a wealth of robot manipulation tasks and continuous control problems. However, it is still limited to industrial applications and suffers from three major challenges: sample inefficiency, re…
  semanticscholar Ruihong Xiao, Chenguang Yang, Yiming Jiang, Hui Zhang 2024-01-24 置信度 0.74 Robotica (Cambridge. Print)
• Simplifying Sim-to-Real Transfer in Autonomous Driving: Coupling Autoware with the CommonRoad Motion Planning Framework
  Validating motion planning algorithms for autonomous vehicles on a real system is essential to improve their safety in the real world. Open-source initiatives, such as Autoware, provide a deployable software stack for real vehicles. However, such driving stack…
  semanticscholar Gerald W¨ursching, Tobias Mascetta, Yuanfei Lin, Matthias Althoff 2024-06-02 置信度 0.74 2024 IEEE Intelligent Vehicles Symposium (IV)
• ADR-PNAS: a Novel Sim-to-Real Transfer Approach for Robotic Manipulation Tasks
  europepmc 2025 置信度 0.80
• Get a Grip: Multi-Finger Grasp Evaluation at Scale Enables Robust Sim-to-Real Transfer
  This work explores conditions under which multi-finger grasping algorithms can attain robust sim-to-real transfer. While numerous large datasets facilitate learning generative models for multi-finger grasping at scale, reliable real-world dexterous grasping re…
  semanticscholar Tyler Ga Wei Lum, Albert H. Li, Preston Culbertson, K. Srinivasan 等 2024-10-31 置信度 0.74 Conference on Robot Learning
• Contrastive Representation Learning for Robust Sim-to-Real Transfer of Adaptive Humanoid Locomotion
  Reinforcement learning has produced remarkable advances in humanoid locomotion, yet a fundamental dilemma persists for real-world deployment: policies must choose between the robustness of reactive proprioceptive control or the proactivity of complex, fragile …
  semanticscholar Yidan Lu, Rurui Yang, Qiran Kou, Mengting Chen 等 2025-09-16 置信度 0.74 arXiv.org
• 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…
  semanticscholar M. Iannotta, Yuxuan Yang, J. A. Stork, Erik Schaffernicht 等 2025-11-06 置信度 0.74 Robotics and Autonomous Systems
• 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.…
  semanticscholar Simranjeet Singh, D. Huang, Florian Richter, Michael C. Yip 2025-09-27 置信度 0.74 arXiv.org
• PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations
  Learning from few demonstrations to develop policies robust to variations in robot initial positions and object poses is a problem of significant practical interest in robotics. Compared to imitation learning, which often struggles to generalize from limited s…
  semanticscholar Haowen Sun, Han Wang, Chengzhong Ma, Shaolong Zhang 等 2025-04-29 置信度 0.74 arXiv.org
• Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning
  Learning contact-rich manipulation skills is essential. Such skills require the robots to interact with the environment with feasible manipulation trajectories and suitable compliance control parameters to enable safe and stable contact. However, learning thes…
  semanticscholar Xiang Zhang, Changhao Wang, Lingfeng Sun, Zheng Wu 等 2023-10-16 置信度 0.74 Conference on Robot Learning
• 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…
  semanticscholar J. Gavura, M. Vavrecka, Igor Farkas, Connor Gäde 2025-07-11 置信度 0.74 International Conference on Artificial Neural Networks
• Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control
  Traffic Signal Control (TSC) is essential for managing urban traffic flow and reducing congestion. Reinforcement Learning (RL) offers an adaptive method for TSC by responding to dynamic traffic patterns, with multi-agent RL (MARL) gaining traction as intersect…
  semanticscholar J. Turnau, Longchao Da, Khoa Vo, Ferdous Al Rafi 等 2025-07-21 置信度 0.74 arXiv.org
• ASTRA: Adversarial Sim-to-Real Transfer Reinforcement Learning for Autoscaling in Cloud Systems
  With the widespread adoption of cloud computing, autoscaling has become crucial for efficient resource management and stable service provision in cloud systems. In recent years, autoscaling methods based on deep reinforcement learning (DRL) have gained signifi…
  semanticscholar Tiangang Li, Shi Ying, Xiangbo Tian, Ting Zhang 等 2025-10-01 置信度 0.74 IEEE Transactions on Software Engineering
• Scalable heliostat surface predictions from focal spots: Sim-to-Real transfer of inverse Deep Learning Raytracing
  Concentrating Solar Power (CSP) plants are a key technology in the transition toward sustainable energy. A critical factor for their safe and efficient operation is the distribution of concentrated solar flux on the receiver. However, flux distributions from i…
  semanticscholar Jan Lewen, Max Pargmann, J. Jitsev, M. Cherti 等 2025-03-28 置信度 0.74 Solar Energy
• Online Behavior-Centric Adaptation for Bipedal Robot Sim-to-Real Transfer With Unmodeled Dynamics Mismatch
  Bipedal robots have achieved remarkable locomotion capabilities through reinforcement learning (RL), yet their real-world deployment remains hindered by the sim-to-real gap—dynamics mismatches between simulation and reality that degrade locomotion performance …
  semanticscholar Xuechao Chen, Yidong Du, Zishun Zhou, Zhicheng Yuan 等 2026 置信度 0.74 IEEE Transactions on Automation Science and Engineering
• Sim-to-real transfer via a Style-Identified Cycle Consistent Generative Adversarial Network: Zero-shot deployment on robotic manipulators through visual domain adaptation
  The sample efficiency challenge in Deep Reinforcement Learning (DRL) compromises its industrial adoption due to the high cost and time demands of real-world training. Virtual environments offer a cost-effective alternative for training DRL agents, but the tran…
  semanticscholar Lucía Güitta-López, Lionel Güitta-López, Jaime Boal, Alvaro Jesús López López 2025-11-01 置信度 0.74 Engineering applications of artificial intelligence
• Development of a Variable‑Stiffness Musculoskeletal Percussion Robot and Realization of Single‑Stroke Motion through Sim‑to‑Real Transfer
  This paper presents a musculoskeletal percussion robot equipped with variable stiffness joints driven by pneumatic artificial muscles (PAMs). To replicate human-like single-stroke drumming, we use deep reinforcement learning in simulation and transfer the lear…
  semanticscholar Tsukasa Biyajima, Rei Yamazaki, M. Okui 2025-10-14 置信度 0.74 Annual Conference of the IEEE Industrial Electronics Society
• Sim-to-Real Transfer Learning for Large-Scale Short-Term Building Energy Forecasting in Sustainable Cities
  Short-term building energy forecasting is essential for optimizing operations, enabling demand response, and improving network reliability. However, its large-scale deployment across building portfolios remains limited, constraining both cost savings and decar…
  semanticscholar Heng Quan, S. Ergan 2025-11-11 置信度 0.74 International Conference on Systems for Energy-Efficient Built Environments
• A Comparative Study of DRL Algorithms for Map-Free Robot Navigation With Zero-Shot Sim-to-Real Transfer
  This paper presents an empirical benchmark of map-free deep reinforcement learning (DRL) for goal-driven indoor navigation using LiDAR-only perception and continuous control, together with a reproducible pipeline for zero-shot sim-to-real transfer. A custom Py…
  semanticscholar Taner Yılmaz, O. Aydogmus 2026 置信度 0.74 IEEE Access
• Few-Shot Magnet Localization Using Sim-to-Real Transfer Learning.
  europepmc 2025 置信度 0.80
• ALDM-Grasping: Diffusion-aided Zero-Shot Sim-to-Real Transfer for Robot Grasping
  To tackle the"reality gap"encountered in Sim-to-Real transfer, this study proposes a diffusion-based framework that minimizes inconsistencies in grasping actions between the simulation settings and realistic environments. The process begins by training an adve…
  semanticscholar Yiwei Li, Zihao Wu, Huaqin Zhao, Tianze Yang 等 2024-03-18 置信度 0.74 arXiv.org
• Machine visual perception from sim-to-real transfer learning for autonomous docking maneuvers
  This paper presents a comprehensive approach to enhancing autonomous docking maneuvers through machine visual perception and sim-to-real transfer learning. By leveraging relative vectoring techniques, we aim to replicate the human ability to execute precise do…
  semanticscholar Derek Worth, Jeffrey Choate, Ryan Raettig, Scott L. Nykl 等 2024-12-03 置信度 0.74 Neural computing & applications (Print)
• The Power of Input: Benchmarking Zero-Shot Sim-to-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control
  In the last decade, data-driven approaches have become popular choices for quadrotor control, thanks to their ability to facilitate the adaptation to unknown or uncertain flight conditions. Among the different data-driven paradigms, Deep Reinforcement Learning…
  semanticscholar Alberto Dionigi, G. Costante, Giuseppe Loianno 2024-10-10 置信度 0.74 IEEE/RJS International Conference on Intelligent RObots and Systems
• Dynamics-Decoupled Trajectory Alignment for Sim-to-Real Transfer in Reinforcement Learning for Autonomous Driving
  Reinforcement learning (RL) has shown promise in robotics, but deploying RL on real vehicles remains challenging due to the complexity of vehicle dynamics and the mismatch between simulation and reality. Factors such as tire characteristics, road surface condi…
  semanticscholar Thomas Steinecker, Alexander Bienemann, Denis Trescher, Thorsten Luettel 等 2025-11-10 置信度 0.74 arXiv.org
• Physics Models for Sim-to-Real Transfer in Professional-Level Robot Table Tennis
  At competitive speeds and spins, a table tennis ball follows complex, counterintuitive trajectories that a robot must track and precisely counter within fractions of a second. Training a reinforcement learning policy capable of these skills is prohibitively ex…
  semanticscholar Christian Conti, Bilan Yang, Alexander Sigrist, Lorenzo Miele 等 2026-06-27 置信度 0.74
• Sim-to-real transfer of co-optimized soft robot crawlers
  semanticscholar Charles B. Schaff, Audrey Sedal, Shiyao Ni, Matthew R. Walter 2023-09-08 置信度 0.74 Autonomous Robots
• simDP: Sim-to-Real Transfer with Shared Action Spaces.
  europepmc 2026 置信度 0.80
• Generative AI pipeline with model-guided filtering for sim-to-real transfer in surgical imaging.
  europepmc 2026 置信度 0.80
• FusionXNet-Edge: A Physics-Guided Lightweight Framework for Predictive Maintenance Under Cross-Domain Deployment Shift
  europepmc 2026 置信度 0.80
• Determining a Clinically Applicable Cutoff in AI Algorithms for Predicting Clinical Deterioration: A Workload-Constrained, Alarm-Based Approach.
  europepmc 2026 置信度 0.80
• SymBridge: Bilateral-Symmetry-Aware Vision–Language–Action Learning with Head-Mounted Display Teleoperation for Sim2Real Transfer in Bimanual Manipulation
  Vision–Language–Action (VLA) models trained on embodied demonstration data exhibit substantial performance degradation when transferred from simulation to reality. We argue that part of this gap is attributable to the implicit and incomplete encoding of geomet…
  europepmc Zijian Zeng, Nikos Mastorakis 2026 置信度 0.80
• From Simulation to Hardware: Gender Dynamics and Transfer of Computational Thinking Skills in Educational Robotics
  europepmc 2026 置信度 0.80
• End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting.
  europepmc 2026 置信度 0.80
• Reluctant Transfer Learning in Penalized Regressions for Individualized Treatment Rules Under Effect Heterogeneity.
  europepmc 2026 置信度 0.80
• Transferable Deep Reinforcement Learning With Edge-Contour-Depth Fusion for Autonomous Wireless Capsule Endoscopy Navigation.
  europepmc 2026 置信度 0.80
• Multiple Summary Knowledge-Based Disease Prevalence Estimation for Group Testing Data.
  europepmc 2026 置信度 0.80