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• ECG-Only Cognitive Workload State Classification in Laparoscopic Training Using Raw and Recurrence-Plot Representations.
  Electrocardiography (ECG)-only workload-state classification offers a lower-burden physiological sensing route than denser multimodal, multi-sensor physiological, or neuroimaging setups for controlled laparoscopic training research. This study evaluated whethe…
  pubmed Jin K, Rubio-Solis A, Naik R, Mylonas G 2026 Jul 12 置信度 0.82
• Design and Testing of an Integrated Robot for Harvesting, Stipe-Cutting, and Grading of Agaricus bisporus.
  To address the limitations of current Agaricus bisporus harvesting robots, including low picking efficiency, susceptibility to mechanical damage, poor operational stability, and discontinuous harvesting processes, an integrated robotic system capable of mushro…
  pubmed Ding T, Zhou Y, Ma H, Ding Y 等 2026 Jul 11 置信度 0.82
• Generative Few-Shot Siamese Networks for Anomaly Detection: Application to Pipeline Leakage in Nuclear Power Plants.
  In safety-critical industrial environments such as nuclear power plants (NPPs), early detection of pipeline leakage is essential for maintaining operational safety. However, leakage events are rare, abnormal samples are difficult to collect, and obtaining suff…
  pubmed Jeong JH, Choi YR, Choi YH, Cho DY 等 2026 Jul 10 置信度 0.82
• From Sensor-Empowered Ubiquitous Computing to Embodied Intelligence: Architectures, Paradigm Evolution, and Emerging Challenges.
  With the rapid development of artificial intelligence technology, the transportation, industry, and healthcare fields are undergoing an intelligent evolution. These advancements have raised higher requirements for technologies such as mobile robots, wearable i…
  pubmed Jia A, Cai Z, Liu X, Zheng K 等 2026 Jul 9 置信度 0.82
• Enhancing Constitutive Description of 5A06 Aluminum Alloy During Warm Deformation Using Machine Learning-Assisted Johnson-Cook Model.
  To accurately characterize the warm deformation behavior and workability of the 5A06 aluminum alloy, this study presents an innovative workflow that develops and systematically validates machine learning-assisted Johnson-Cook (ML-JC) frameworks based on artifi…
  pubmed Liu Z, Deng L, Long J, Gao C 等 2026 Jul 10 置信度 0.82
• Accuracy of Robot-Assisted Pedicle Screw Placement: Two-Center Experience with Learning Curve Analysis.
  Background : Accurate pedicle screw placement remains essential in spinal instrumentation, and robotic navigation has been introduced to improve safety, reproducibility, and workflow standardization. This study evaluated the accuracy of robot-assisted pedicle …
  pubmed Zaed I, Brembilla C, De Gennaro Aquino G, Pizzica E 等 2026 Jul 22 置信度 0.82
• Safe Implementation of Robotic Colorectal Surgery: Balancing Surgical Training and Patient Safety with the Dual-Console System.
  Background : Robotic platforms expand minimally invasive options in colorectal surgery but raise concerns about training and patient safety. Dual-console systems may enable real-time coaching while preserving outcomes. Methods : We implemented a structured fra…
  pubmed Košir JA, Petrič M, Trotovšek B, Norčič G 等 2026 Jul 20 置信度 0.82
• Binocular Vision-Based Image Extraction and Feature Analysis of Weld Beads in 316L Wire Arc Additive Manufacturing.
  To address the challenges of low image quality and difficult feature extraction of weld beads caused by the complex dynamics of the molten pool, intense arc light, and spatter interference during wire arc additive manufacturing (WAAM) of 316L stainless steel, …
  pubmed Yue Y, Zhu Q, Li H 2026 Jul 20 置信度 0.82
• Evaluating Surgical Approaches in Advanced Age: A Propensity-Matched Analysis of Postoperative Morbidity and Short-Term Outcomes Following Open Versus Robotic Radical Cystectomy (ASPECT Study).
  Introduction: The optimal surgical approach for elderly bladder cancer patients remains controversial. We compared perioperative morbidity and short-term outcomes in patients aged ≥ 75 years undergoing open radical cystectomy (ORC) versus robot-assisted…
  pubmed Barakat B, Bauer J, Sayed M, Hakoub R 等 2026 Jul 17 置信度 0.82
• The Progress of Pancreatectomy for Pancreatic Cancer Treatment-Lessons Learned and Future Challenges.
  Since decades, surgery of pancreatic adenocarcinoma is confronted with two major challenges. First, the prognosis of pancreatic adenocarcinoma is the worst of all gastrointestinal malignancies, characterized by late diagnosis and aggressive tumor biology. Seco…
  pubmed Függer R, Biebl M 2026 Jul 16 置信度 0.82
• An Architecture for a Quantum Teleo-Reactive Robot.
  A reactive agent operating in a complex environment must classify its perceived state and select an action under uncertainty. This uncertainty may arise from sensor noise, ambiguous perceptual configurations, or the limited separability of the action regions i…
  pubmed Chella A, Gaglio S, Pilato G, Vella F 2026 Jun 27 置信度 0.82
• SHAP-Based Feature Augmentation and Stacking Ensemble Learning for ECG-Based Serum Potassium Abnormality Prediction.
  Electrocardiogram (ECG) signals contain important clinical information associated with serum potassium abnormalities. However, in Taiwan, raw patient data and original medical signals generally cannot be taken outside the hospital environment, thereby limiting…
  pubmed Ko YH, Hung CS, Lin CR, Ciou YF 等 2026 Jul 16 置信度 0.82
• Detecting and Improving Human Cognitive State in Real-Time Using Data-Driven Adaptive Systems: A Systematic Review.
  Changes in human attention, workload, or alertness over time can affect task performance and may even increase the risk of injury. Detecting these changes in real time can be beneficial in improving system performance and safety. We reviewed 27 studies that de…
  pubmed Kulkarni AR, Kuber PM 2026 Jun 24 置信度 0.82
• Mapping the learning curve of robotic cholecystectomy: a multi‑surgeon cohort analysis.
  Robotic cholecystectomy is increasingly adopted as an alternative to laparoscopic cholecystectomy and proposed as an entry‑level procedure in robotic training curricula, yet real‑world data on surgeon‑specific learning curves and their imp…
  pubmed Schena CA, Mita MT, De Palma C, Bianco G 等 2026 Jul 27 置信度 0.82
• A survey of robot learning from demonstration
  openalex Brenna Argall, Sonia Chernova, Manuela Veloso, Brett Browning 2008-12-03 置信度 0.72 Computer scienceCategorizationTeleoperationRobotMatching (statistics)
• Bettering operation of Robots by learning
  Abstract This article proposes a betterment process for the operation of a mechanical robot in a sense that it betters the next operation of a robot by using the previous operation's data. The process has an iterative learning structure such that the ( k + 1)t…
  openalex Suguru Arimoto, Sadao Kawamura, Fumio Miyazaki 1984-06-01 置信度 0.72 TrajectoryProcess (computing)Convergence (economics)RobotMotion (physics)
• Reinforcement learning in robotics: A survey
  Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforce…
  openalex Jens Kober, J. Andrew Bagnell, Jan Peters 2013-08-23 置信度 0.72 Reinforcement learningArtificial intelligenceRoboticsFunction (biology)Computer science
• Recent Advances in Robot Learning from Demonstration
  In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be neith…
  openalex Harish Ravichandar, Athanasios Polydoros, Sonia Chernova, Aude Billard 2019-12-06 置信度 0.72 Artificial intelligenceRobotRoboticsComputer scienceRobot learning
• Lifelong robot learning
  openalex Sebastian Thrun, Tom M. Mitchell 1995-07-01 置信度 0.72 Computer scienceRobotRobot learningArtificial intelligenceMobile robot
• Statistical Learning Theory
  A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics…
  openalex Yuhai Wu, Vladimir Vapnik 1999-11-01 置信度 0.72 GeneralizationStatistical learning theoryArtificial intelligenceConsistency (knowledge bases)Computer science
• Deep learning for detecting robotic grasps
  We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. F…
  openalex Ian Lenz, Honglak Lee, Ashutosh Saxena 2015-03-16 置信度 0.72 Artificial intelligenceComputer scienceRegularization (linguistics)Deep learningRGB color model
• RLBench: The Robot Learning Benchmark & Learning Environment
  We present a challenging new benchmark and learning-environment for robot learning: RLBench. The benchmark features 100 completely unique, hand-designed tasks, ranging in difficulty from simple target reaching and door opening to longer multi-stage tasks, such…
  openalex Stephen James, Zicong Ma, David Rovick Arrojo, Andrew J. Davison 2020-02-18 置信度 0.72 Computer scienceArtificial intelligenceBenchmark (surveying)Task (project management)Robot learning
• Robot Learning from Demonstration in Robotic Assembly: A Survey
  Learning from demonstration (LfD) has been used to help robots to implement manipulation tasks autonomously, in particular, to learn manipulation behaviors from observing the motion executed by human demonstrators. This paper reviews recent research and develo…
  openalex Zuyuan Zhu, Huosheng Hu 2018-04-16 置信度 0.72 RobotImitationFocus (optics)Artificial intelligenceRobot learning
• A Survey of Robot Learning Strategies for Human-Robot Collaboration in Industrial Settings
  openalex Debasmita Mukherjee, Kashish Gupta, Li Chang, Homayoun Najjaran 2021-07-31 置信度 0.72 RobotHuman–computer interactionHuman–robot interactionRobot learningComputer science
• Learning agile and dynamic motor skills for legged robots
  Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship an…
  openalex Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, C. Dario Bellicoso 等 2019-01-17 置信度 0.72 Reinforcement learningRobotComputer scienceAgile software developmentLegged robot
• Robot Learning
  openalex Jonathan H. Connell, Sridhar Mahadevan 1993-06-01 置信度 0.72 Artificial intelligenceComputer sciencePsychology
• Reinforcement learning for robots using neural networks
  Reinforcement learning agents are adaptive, reactive, and self-supervised. The aim of this dissertation is to extend the state of the art of reinforcement learning and enable its applications to complex robot-learning problems. In particular, it focuses on two…
  openalex Long-Ji Lin 1992-01-01 置信度 0.72 Reinforcement learningComputer scienceArtificial intelligenceMarkov decision processSpeedup
• Robot learning towards smart robotic manufacturing: A review
  Robotic equipment has been playing a central role since the proposal of smart manufacturing. Since the beginning of the first integration of industrial robots into production lines, industrial robots have enhanced productivity and relieved humans from heavy wo…
  openalex Zhihao Liu, Quan Liu, Wenjun Xu, Lihui Wang 等 2022-04-20 置信度 0.72 Robot learningArtificial intelligenceRobotRoboticsIndustrial robot
• Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
  Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning proces…
  openalex Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine 2017-05-01 置信度 0.72 Reinforcement learningComputer scienceArtificial intelligenceRobotAsynchronous communication
• Blocks to Robots: Learning with Technology in the Early Childhood Classroom
  Research shows that attitudes about science, math, and technology start to form during the early schooling years. This pioneering book shows how to successfully use technology in the early childhood classroom. Grounded in a constructivist approach to teaching …
  openalex Marina Umaschi Bers 2008-02-01 置信度 0.72 Socioemotional selectivity theoryEarly childhoodMathematics educationEarly childhood educationPsychology
• Artificial intelligence, machine learning and deep learning in advanced robotics, a review
  Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of advanced robotics in recent years. AI, ML, and DL are transforming the field of advanced robotics, making robots more intelligent, efficient, and adapt…
  openalex Mohsen Soori, Behrooz Arezoo, Roza Dastres 2023-01-01 置信度 0.72 Artificial intelligenceRoboticsRobotComputer scienceApplications of artificial intelligence
• Robot learning from demonstration by constructing skill trees
  We describe CST, an online algorithm for constructing skill trees from demonstration trajectories. CST segments a demonstration trajectory into a chain of component skills, where each skill has a goal and is assigned a suitable abstraction from an abstraction …
  openalex George Konidaris, Scott Kuindersma, Roderic A. Grupen, Andrew G. Barto 2011-12-05 置信度 0.72 AbstractionTrajectoryComputer scienceComponent (thermodynamics)Domain (mathematical analysis)
• Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
  The last half decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of the recen…
  openalex Lukas Brunke, Melissa Greeff, Adam W. Hall, Zhaocong Yuan 等 2022-01-26 置信度 0.72 Reinforcement learningRobot learningArtificial intelligenceComputer scienceLeverage (statistics)
• Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
  We presentOrbit, a unified and modular framework for robot learning powered byNvidiaIsaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and high-fidelity rigid and deformable body simulation. …
  openalex Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu 等 2023-04-25 置信度 0.72 Computer scienceArtificial intelligenceReinforcement learningModular designBenchmark (surveying)
• Learning and executing generalized robot plans
  openalex Richard Fikes, Peter E. Hart, Nils J. Nilsson 1972-01-01 置信度 0.72 STRIPSPlan (archaeology)Computer scienceProcess (computing)Robot
• Continuous control for robot based on deep reinforcement learning
  One of the main targets of artificial intelligence is to solve the complex control problems which have high-dimensional observation spaces. Recently, the combination of deep learning and reinforcement learning has made remarkable progress, including the high-l…
  openalex Shansi Zhang 2019-01-01 置信度 0.72 Reinforcement learningComputer scienceReinforcementControl (management)Artificial intelligence
• Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
  openalex Stefan Schaal, Christopher G. Atkeson, Sethu Vijayakumar 2002-07-01 置信度 0.72 Computer scienceArtificial intelligenceHumanoid robotRobotMachine learning
• Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations
  We present a system for robust robot skill acquisition from kinesthetic demonstrations. This system allows a robot to learn a simple goal-directed gesture and correctly reproduce it despite changes in the initial conditions and perturbations in the environment…
  openalex Micha Hersch, F. Guenter, Sylvain Calinon, Aude Billard 2008-12-01 置信度 0.72 Kinesthetic learningInverse kinematicsHumanoid robotComputer scienceArtificial intelligence
• The limits and potentials of deep learning for robotics
  <p>The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learni…
  openalex Suenderhauf, Niko, Oliver Brock, Walter J. Scheirer, Raia Hadsell 等 2018-04-01 置信度 0.72 Artificial intelligenceRoboticsDeep learningComputer scienceMachine learning
• Robot Learning from Human Teachers
  openalex Sonia Chernova, Andrea L. Thomaz 2014-01-01 置信度 0.72 Task (project management)Field (mathematics)RobotHuman–computer interactionComputer science
• Machine learning a probabilistic perspective
  Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This text…
  openalex Kevin P. Murphy 2012-01-01 置信度 0.72 Computer scienceProbabilistic logicArtificial intelligenceField (mathematics)Conditional random field
• Learning metric-topological maps for indoor mobile robot navigation
  openalex Sebastian Thrun 1998-02-01 置信度 0.72 Topological mapComputer scienceGridMetric (unit)Mobile robot
• Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
  Current model free learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp location…
  openalex Lerrel Pinto, Abhinav Gupta 2016-05-01 置信度 0.72 OverfittingGRASPComputer scienceArtificial intelligenceConvolutional neural network
• Artificial intelligence: a modern approach
  The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search…
  openalex Dr. Anil Kumar, Sivasubramanian Balasubramanian, Dr. Haewon Byeon, Prof. Ganesh Vasudeo Manerkar 1995-11-01 置信度 0.72 Artificial intelligenceComputer scienceInferenceArtificial intelligence, situated approachProbabilistic logic
• Storytelling with robots: Learning companions for preschool children's language development
  Children's oral language skills in preschool can predict their academic success later in life. As such, increasing children's skills early on could improve their success in middle and high school. To this end, we propose that a robotic learning companion could…
  openalex Jacqueline Kory, Cynthia Breazeal 2014-08-01 置信度 0.72 StorytellingRobotVocabularyNarrativePsychology
• Reinforcement Learning in the Multi-Robot Domain
  openalex Maja J. Matarić 1997-03-01 置信度 0.72 Reinforcement learningComputer scienceArtificial intelligenceTask (project management)Robot
• A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
  A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to e…
  openalex Oliver Kroemer, Scott Niekum, George Konidaris 2021-01-01 置信度 0.72 RobotExploitArtificial intelligenceComputer scienceRobotics
• Tutelage and socially guided robot learning
  We view the problem of machine learning as a collaboration between the human and the machine. Inspired by human-style tutelage, we situate the learning problem within a dialog in which social interaction structures the learning experience, providing instructio…
  openalex Andrea Lockerd, Cynthia Breazeal 2005-04-06 置信度 0.72 Dialog boxTask (project management)Computer scienceRobotHumanoid robot
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
  TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging fr…
  openalex Martı́n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo 等 2016-03-14 置信度 0.72 Scale (ratio)Computer scienceArtificial intelligenceMachine learningDistributed computing
• Gaussian Processes for Data-Efficient Learning in Robotics and Control
  Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) appr…
  openalex Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen 2013-11-19 置信度 0.72 Artificial intelligenceComputer scienceMachine learningReinforcement learningRobotics
• A DMPs-Based Framework for Robot Learning and Generalization of Humanlike Variable Impedance Skills
  One promising approach for robots efficiently learning skills is to learn manipulation skills from human tutors by demonstration and then generalize these learned skills to complete new tasks. Traditional learning and generalization methods, however, have not …
  openalex Chenguang Yang, Chao Zeng, Cheng Fang, Wei He 等 2018-03-20 置信度 0.72 TrajectoryGeneralizationRobotComputer scienceStiffness
• Electronic skins and machine learning for intelligent soft robots
  Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulati…
  openalex Benjamin Shih, Dylan Shah, Jinxing Li, Thomas George Thuruthel 等 2020-04-15 置信度 0.72 RobotProprioceptionComputer scienceHuman–computer interactionArtificial intelligence
• Controlling an organic synthesis robot with machine learning to search for new reactivity
  openalex Jarosław M. Granda, Liva Donina, Vincenza Dragone, De‐Liang Long 等 2018-07-01 置信度 0.72 Reactivity (psychology)Computer scienceArtificial intelligenceMachine learningChemical space
• Model learning for robot control: a survey
  openalex Duy Nguyen-Tuong, Jan Peters 2011-04-12 置信度 0.72 Behavioural sciencesControl (management)PsychologyArtificial intelligenceComputer science
• Multi-robot learning with particle swarm optimization
  We apply an adapted version of Particle Swarm Optimization to distributed unsupervised robotic learning in groups of robots with only local information. The performance of the learning technique for a simple task is compared across robot groups of various size…
  openalex Jim Pugh, Alcherio Martinoli 2006-05-08 置信度 0.72 Particle swarm optimizationComputer scienceRobotMulti-swarm optimizationArtificial intelligence
• Robot learning of industrial assembly task via human demonstrations
  Human–robot collaboration in industrial applications is a challenging robotic task. Human working together with the robot at a workplace to complete a task may create unpredicted events for the robot, as humans can act unpredictably. Humans tend to perform a t…
  openalex Maria Kyrarini, Muhammad Haseeb, Danijela Ristić–Durrant, Axel Gräser 2018-04-06 置信度 0.72 Computer scienceRobotTask (project management)Human–computer interactionArtificial intelligence
• Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning
  Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, the…
  openalex Changan Chen, Yuejiang Liu, S. Kreiss, Alexandre Alahi 2019-05-01 置信度 0.72 Reinforcement learningCrowdsRobotComputer scienceArtificial intelligence
• Using locally weighted regression for robot learning
  The use of locally weighted regression in memory-based robot learning is explored. A local model is formed to answer each query, using a weighted regression in which close points (similar experiences) are weighted more than distant points (less relevant experi…
  openalex Christopher G. Atkeson 2002-12-10 置信度 0.72 Metric (unit)Similarity (geometry)Artificial intelligenceRobotComputer science
• Robotic learning of haptic adjectives through physical interaction
  openalex Vivian Chu, Ian McMahon, Lorenzo Riano, Craig G. McDonald 等 2014-10-16 置信度 0.72 Computer scienceHaptic technologyHuman–computer interactionArtificial intelligence
• Robot learning by demonstration
  openalex Aude Billard, Daniel H. Grollman 2013-01-01 置信度 0.72 Computer scienceRobotArtificial intelligenceHuman–computer interactionPsychology
• Learning robust perceptive locomotion for quadrupedal robots in the wild
  Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into underexplored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion: Perceiving the terrain …
  openalex Takahiro Miki, Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen 等 2022-01-19 置信度 0.72 RobotTerrainComputer scienceArtificial intelligencePerception
• Continual Learning for Robotics: Definition, Framework, Learning\n Strategies, Opportunities and Challenges
  Continual learning (CL) is a particular machine learning paradigm where the\ndata distribution and learning objective changes through time, or where all the\ntraining data and objective criteria are never available at once. The evolution\nof the learning proce…
  openalex Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni 等 2019-06-29 置信度 0.72 Artificial intelligenceComputer scienceForgettingMachine learningRobot learning
• Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation
  We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners…
  openalex Lei Tai, Giuseppe Paolo, Ming Liu 2017-09-01 置信度 0.72 Mobile robotComputer scienceReinforcement learningArtificial intelligenceComputer vision
• Particle swarm optimization for unsupervised robotic learning
  We explore using particle swarm optimization on problems with noisy performance evaluation, focusing on unsupervised robotic learning. We adapt a technique of overcoming noise used in genetic algorithms for use with particle swarm optimization, and evaluate th…
  openalex Jim Pugh, Alcherio Martinoli, Yizhen Zhang 2005-08-30 置信度 0.72 Particle swarm optimizationComputer scienceNoise (video)Unsupervised learningArtificial intelligence
• Learning ambidextrous robot grasping policies
  Universal picking (UP), or reliable robot grasping of a diverse range of novel objects from heaps, is a grand challenge for e-commerce order fulfillment, manufacturing, inspection, and home service robots. Optimizing the rate, reliability, and range of UP is d…
  openalex Jeffrey Mahler, Matthew Matl, Vishal Satish, Michael Danielczuk 等 2019-01-17 置信度 0.72 Computer scienceArtificial intelligencePsychologyHuman–computer interactionBusiness
• Integration of Machine Learning and Optimization for Robot Learning
  openalex Amir Mosavi, Annamária R. Várkonyi-Kóczy 2016-09-22 置信度 0.72 Artificial intelligenceRoboticsRobot learningComputer scienceMachine learning
• Survey of Model-Based Reinforcement Learning: Applications on Robotics
  openalex Athanasios Polydoros, Lazaros Nalpantidis 2017-01-26 置信度 0.72 Reinforcement learningArtificial intelligenceComputer scienceRoboticsAdaptability
• Robot Learning Driven by Emotions
  The adaptive value of emotions in nature indicates that they might also be useful in artificial creatures. Experiments were carried out to investigate this hypothesis in a simulated learning robot. For this purpose, a non-symbolic emotion model was developed t…
  openalex Sandra Clara Gadanho, John Hallam 2001-03-01 置信度 0.72 Reinforcement learningComputer scienceRobotArtificial intelligencePerception
• Natural methods for robot task learning
  Among humans, teaching various tasks is a complex process which relies on multiple means for interaction and learning, both on the part of the teacher and of the learner. Used together, these modalities lead to effective teaching and learning approaches, respe…
  openalex Monica Nicolescu, Maja J. Matarić 2003-07-14 置信度 0.72 Computer scienceTask (project management)GeneralizationModalitiesRobot
• Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
  This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into co…
  openalex Wei He, Yiting Dong 2017-03-01 置信度 0.72 Control theory (sociology)Stability (learning theory)Artificial neural networkRobotComputer science
• Robotics, Motor Learning, and Neurologic Recovery
  Robotic devices are helping shed light on human motor control in health and injury. By using robots to apply novel force fields to the arm, investigators are gaining insight into how the nervous system models its external dynamic environment. The nervous syste…
  openalex David J. Reinkensmeyer, J.L. Emken, Steven C. Cramer 2004-07-15 置信度 0.72 Motor learningRoboticsRobotComputer scienceArtificial intelligence
• Robotic neurorehabilitation: a computational motor learning perspective
  Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability acro…
  openalex Vincent Huang, John W. Krakauer 2009-02-25 置信度 0.72 NeurorehabilitationMotor learningContext (archaeology)RehabilitationPhysical medicine and rehabilitation
• A guide to deep learning in healthcare
  openalex Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov 等 2018-12-24 置信度 0.72 Deep learningComputer scienceArtificial intelligenceContext (archaeology)Reinforcement learning
• Robot learning from demonstration
  openalex Aude Billard, Roland Siegwart 2004-05-29 置信度 0.72 Computer scienceKinematicsSimilarity (geometry)Cartesian coordinate systemMotion (physics)
• Adaptive iterative learning control for robot manipulators
  openalex Abdelhamid Tayebi 2004-04-10 置信度 0.72 Iterative learning controlControl theory (sociology)Adaptive controlTrajectoryVariable (mathematics)
• Robot Learning by Demonstration with local Gaussian process regression
  In recent years there was a tremendous progress in robotic systems, and however also increased expectations: A robot should be easy to program and reliable in task execution. Learning from Demonstration (LfD) offers a very promising alternative to classical en…
  openalex Matti Schneider, Wolfgang Ertel 2010-10-01 置信度 0.72 RobotComputer scienceGaussian processArtificial intelligenceProgramming by demonstration
• PyRep: Bringing V-REP to Deep Robot Learning
  PyRep is a toolkit for robot learning research, built on top of the virtual robotics experimentation platform (V-REP). Through a series of modifications and additions, we have created a tailored version of V-REP built with robot learning in mind. The new PyRep…
  openalex Stephen James, Marc Freese, Andrew J. Davison 2019-06-26 置信度 0.72 Python (programming language)Artificial intelligenceComputer scienceRobotRobotics
• Social Robots for Language Learning: A Review
  In recent years, robots have increasingly been implemented as tutors in both first- and second-language education. The field of robot-assisted language learning (RALL) is developing rapidly. Studies have been published targeting different languages, age groups…
  openalex Rianne van den Berghe, Josje Verhagen, Ora Oudgenoeg‐Paz, Sanne H.G. van der Ven 等 2018-12-29 置信度 0.72 RobotVocabularyLanguage acquisitionGrammarPsychology
• Reinforcement Learning: A Survey
  This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarize…
  openalex Leslie Pack Kaelbling, Michael L. Littman, Andrew Moore 1996-05-01 置信度 0.72 Reinforcement learningComputer scienceReinforcementArtificial intelligenceMarkov decision process
• Deep Reinforcement Learning: A Brief Survey
  Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world. Currently, deep learning is enabling reinforc…
  openalex Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil A. Bharath 2017-11-01 置信度 0.72 Reinforcement learningComputer scienceArtificial intelligenceDeep learningField (mathematics)
• A Joyful Classroom Learning System with Robot Learning Companion for Children to Learn Mathematics Multiplication
  This research demonstrates the design of a Joyful Classroom Learning System (JCLS) with flexible, mobile and joyful features. The theoretical foundations of this research include the experiential learning theory, constructivist learning theory and joyful learn…
  openalex Chun‐Wang Wei, I‐Chun Hung, Ling Lee, Nian‐Shing Chen 2011-04-01 置信度 0.72 Experiential learningComputer scienceClass (philosophy)Robot learningEducational technology
• Task-level robot learning
  The functionality of robots can be improved by programming them to learn tasks from practice. Task-level learning can compensate for the structural modeling errors of the robot's lower-level control systems and can speed up the learning process by reducing the…
  openalex E.W. Aboaf, C.G. Atkeson, David J. Reinkensmeyer 2003-01-06 置信度 0.72 RobotComputer scienceTask (project management)Artificial intelligenceRobot learning
• Asymmetric Actor Critic for Image-Based Robot Learning
  Deep reinforcement learning (RL) has proven a powerful technique in many sequential decision making domains. However, robotics poses many challenges for RL, most notably training on a physical system can be expensive and dangerous, which has sparked significan…
  openalex Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba 等 2018-06-26 置信度 0.72 ObservabilityReinforcement learningComputer scienceArtificial intelligenceRobot
• A Survey on Learning-Based Robotic Grasping
  Abstract Purpose of Review This review provides a comprehensive overview of machine learning approaches for vision-based robotic grasping and manipulation. Current trends and developments as well as various criteria for categorization of approaches are provide…
  openalex Kilian Kleeberger, Richard Bormann, Werner Kraus, Marco F. Huber 2020-09-20 置信度 0.72 Computer scienceSoftware deploymentArtificial intelligenceGeneralizationCategorization
• Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning
  Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating …
  openalex Yicheng Zhao, Jiyun Zhang, Zhengwei Xu, Shijing Sun 等 2021-04-13 置信度 0.72 Perovskite (structure)Thermal stabilityPhotovoltaicsStability (learning theory)Decomposition
• Deep reinforcement learning based mobile robot navigation: A review
  Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robo…
  openalex Kai Zhu, Tao Zhang 2021-04-21 置信度 0.72 Mobile robot navigationReinforcement learningMobile robotObstacle avoidanceComputer science
• A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
  We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image salien…
  openalex Alessandro Giusti, Jérôme Guzzi, Dan Cireşan, Fang-Lin He 等 2015-12-17 置信度 0.72 Artificial intelligenceComputer scienceMonocularComputer visionSegmentation
• Grounded Action Transformation for Robot Learning in Simulation
  Robot learning in simulation is a promising alternative to the prohibitive sample cost of learning in the physical world. Unfortunately, policies learned in simulation often perform worse than hand-coded policies when applied on the physical robot. Grounded si…
  openalex Josiah P. Hanna, Peter Stone 2017-02-12 置信度 0.72 RobotFidelityComputer scienceAction (physics)Robot learning
• A new optimizer using particle swarm theory
  The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and …
  openalex R.C. Eberhart, James Kennedy 2002-11-19 置信度 0.72 Particle swarm optimizationBenchmark (surveying)Computer scienceMulti-swarm optimizationEvolutionary computation
• Learning curve for robotic-assisted laparoscopic colorectal surgery
  BACKGROUND: Robotic-assisted laparoscopic surgery (RALS) is evolving as an important surgical approach in the field of colorectal surgery. We aimed to evaluate the learning curve for RALS procedures involving resections of the rectum and rectosigmoid. METHODS:…
  openalex Malak B. Bokhari, Chirag B. Patel, Diego I. Ramos‐Valadez, Madhu Ragupathi 等 2010-08-24 置信度 0.72 CUSUMMedicineLearning curveColorectal surgeryRectum
• Effective reinforcement learning for mobile robots
  Programming mobile robots can be a long, time-consuming process. Specifying the low-level mapping from sensors to actuators is prone to programmer misconceptions, and debugging such a mapping can be tedious. The idea of having a robot learn how to accomplish a…
  openalex William D. Smart, Leslie Pack Kaelbling 2003-06-25 置信度 0.72 ProgrammerDebuggingComputer scienceRobotReinforcement learning
• Q-learning
  openalex Christopher J. Watkins, Peter Dayan 1992-05-01 置信度 0.72 SketchAction (physics)Markov processMarkov decision processConvergence (economics)
• Real-time robot learning with locally weighted statistical learning
  Locally weighted learning (LWL) is a class of statistical learning techniques that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several…
  openalex Stefan Schaal, Christopher G. Atkeson, Sethu Vijayakumar 2002-11-07 置信度 0.72 Artificial intelligenceHumanoid robotRobotMathematicsComputer science
• Robot Learning Manipulation Action Plans by "Watching" Unconstrained Videos from the World Wide Web
  In order to advance action generation and creation in robots beyond simple learned schemas we need computational tools that allow us to automatically interpret and represent human actions. This paper presents a system that learns manipulation action plans by p…
  openalex Yezhou Yang, Yi Li, Cornelia Fermüller, Yiannis Aloimonos 2015-03-04 置信度 0.72 Computer scienceArtificial intelligenceParsingAction (physics)GRASP
• A robot learning from demonstration framework to perform force-based manipulation tasks
  openalex Leonel Rozo, Pablo Jiménez, Carme Torras 2013-01-01 置信度 0.72 Computer scienceProgramming by demonstrationRobotHaptic technologyKinesthetic learning
• Sim-to-Real Robot Learning from Pixels with Progressive Nets
  Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to achieve performance on a real robot, but their potential has been demonstrated in s…
  openalex Andrei A. Rusu, Vecerik, Mel, Thomas Rothörl, Nicolas Heess 等 2016-10-13 置信度 0.72 Reinforcement learningComputer scienceRobotArtificial intelligenceRobot learning
• Biologically Inspired Motion Modeling and Neural Control for Robot Learning From Demonstrations
  In this paper, we propose a biologically inspired framework for robot learning based on demonstrations. The dynamic movement primitive (DMP), which is motivated by neurobiology and human behavior, is employed to model a robotic motion that is generalizable. Ho…
  openalex Chenguang Yang, Chuize Chen, Ning Wang, Zhaojie Ju 等 2018-08-21 置信度 0.72 Computer scienceCerebellar model articulation controllerRobotArtificial intelligenceArtificial neural network
• Scaling Robot Learning with Semantically Imagined Experience
  Recent advances in robot learning have shown promise in enabling robots to perform a variety of manipulation tasks and generalize to novel scenarios.One of the key contributing factors to this progress is the scale of robot data used to train the models.To obt…
  openalex Tianhe Yu, Ted Xiao, Jonathan Tompson, Austin V. Stone 等 2023-07-10 置信度 0.72 RobotComputer scienceScalingArtificial intelligenceNatural language processing
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models
  Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backb…
  openalex Embodiment Collaboration, O'Neill, Abby, Rehman, Abdul, Gupta, Abhinav 等 2023-10-13 置信度 0.72 RoboticsArtificial intelligenceRobotComputer scienceConsolidation (business)
• QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
  In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static…
  openalex Dmitry Kalashnikov, Alex Irpan, Peter Pástor, Julian Ibarz 等 2018-06-27 置信度 0.72 GRASPReinforcement learningArtificial intelligenceComputer scienceLeverage (statistics)