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• The Principle and Application of Maser Navigation
  The traditional celestial navigation system (CNS) is used the moon, stars, and planets as celestial guides. Then the star tracker (i.e. track one star or planet or angle between it) and star sensor (i.e. sense many star simultaneous) be used to determine the a…
  arxiv Jiang Dong 2008-12-31T06:53:24Z 置信度 0.78 physics.gen-phastro-ph.IM
• Embodied Navigation Foundation Model
  Navigation is a fundamental capability in embodied AI, representing the intelligence required to perceive and interact within physical environments following language instructions. Despite significant progress in large Vision-Language Models (VLMs), which exhi…
  arxiv Jiazhao Zhang, Anqi Li, Yunpeng Qi, Minghan Li 等 2025-09-15T16:52:43Z 置信度 0.78 cs.RO
• NavTrust: Benchmarking Trustworthiness for Embodied Navigation
  There are two major categories of embodied navigation: Vision-Language Navigation (VLN), where agents navigate by following natural language instructions; and Object-Goal Navigation (OGN), where agents navigate to a specified target object. However, existing w…
  arxiv Huaide Jiang, Yash Chaudhary, Yuping Wang, Zehao Wang 等 2026-03-19T17:59:51Z 置信度 0.78 cs.ROcs.AIcs.CVcs.LGeess.SY
• Pushing it out of the Way: Interactive Visual Navigation
  We have observed significant progress in visual navigation for embodied agents. A common assumption in studying visual navigation is that the environments are static; this is a limiting assumption. Intelligent navigation may involve interacting with the enviro…
  arxiv Kuo-Hao Zeng, Luca Weihs, Ali Farhadi, Roozbeh Mottaghi 2021-04-28T22:46:41Z 置信度 0.78 cs.CVcs.AIcs.RO
• CoNav: Collaborative Cross-Modal Reasoning for Embodied Navigation
  Embodied navigation demands comprehensive scene understanding and precise spatial reasoning. While image-text models excel at interpreting pixel-level color and lighting cues, 3D-text models capture volumetric structure and spatial relationships. However, unif…
  arxiv Haihong Hao, Mingfei Han, Changlin Li, Zhihui Li 等 2025-05-22T13:27:54Z 置信度 0.78 cs.CVcs.MM
• Towards Physically Realizable Adversarial Attacks in Embodied Vision Navigation
  The significant advancements in embodied vision navigation have raised concerns about its susceptibility to adversarial attacks exploiting deep neural networks. Investigating the adversarial robustness of embodied vision navigation is crucial, especially given…
  arxiv Meng Chen, Jiawei Tu, Chao Qi, Yonghao Dang 等 2024-09-16T08:21:22Z 置信度 0.78 cs.CVcs.RO
• Incorporating Gaze into Social Navigation
  Most current approaches to social navigation focus on the trajectory and position of participants in the interaction. Our current work on the topic focuses on integrating gaze into social navigation, both to cue nearby pedestrians as to the intended trajectory…
  arxiv Justin Hart, Reuth Mirsky, Xuesu Xiao, Peter Stone 2021-07-08T17:46:32Z 置信度 0.78 cs.RO
• Nav-R1: Reasoning and Navigation in Embodied Scenes
  Embodied navigation requires agents to integrate perception, reasoning, and action for robust interaction in complex 3D environments. Existing approaches often suffer from incoherent and unstable reasoning traces that hinder generalization across diverse envir…
  arxiv Qingxiang Liu, Ting Huang, Zeyu Zhang, Hao Tang 2025-09-13T16:31:03Z 置信度 0.78 cs.ROcs.CV
• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration
  Robotic learning for navigation in unfamiliar environments needs to provide policies for both task-oriented navigation (i.e., reaching a goal that the robot has located), and task-agnostic exploration (i.e., searching for a goal in a novel setting). Typically,…
  arxiv Ajay Sridhar, Dhruv Shah, Catherine Glossop, Sergey Levine 2023-10-11T21:07:14Z 置信度 0.78 cs.ROcs.CVcs.LG
• Context Detection for Advanced Self-Aware Navigation using Smartphone Sensors
  Navigation and positioning systems dependent on both the operating environment and the behaviour of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning and the behaviour can contribute additional…
  arxiv Han Gao, Paul D. Groves 2020-05-15T13:38:52Z 置信度 0.78 eess.SP
• Trade-off Analysis for Lunar Augmented Navigation Service (LANS) Constellation Design
  The establishment of a sustainable human presence on the Moon demands robust positioning, navigation, and timing (PNT) services capable of supporting both surface and orbital operations. This paper presents a comprehensive trade-off analysis of lunar frozen-or…
  arxiv Keidai Iiyama, Grace Gao 2025-10-15T21:59:42Z 置信度 0.78 astro-ph.IMastro-ph.EP
• MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
  We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. The simulator leverages large datasets of complex 3D environments and supports flexible configuration of multi…
  arxiv Manolis Savva, Angel X. Chang, Alexey Dosovitskiy, Thomas Funkhouser 等 2017-12-11T18:24:58Z 置信度 0.78 cs.LGcs.AIcs.CVcs.GRcs.RO
• EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness
  Cross-embodiment navigation is a key challenge in embodied intelligence. Due to differences in embodiment, the same visual observation may imply different actions for different agents, making prediction ambiguous when relying solely on vision. Existing studies…
  arxiv Jialu Zhang, Yong Du, Xianda Guo, Shunwang Sun 等 2026-07-22T08:10:49Z 置信度 0.78 cs.ROcs.CV
• NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks
  Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isolation, with disparate datasets and training paradigms. In this paper, we observe that both tasks can be formulated as …
  arxiv Zhihao Luo, Wentao Yan, Jingyu Gong, Min Wang 等 2025-08-04T04:28:18Z 置信度 0.78 cs.ROcs.LG
• Quantum Artificial Intelligence for Secure Autonomous Vehicle Navigation: An Architectural Proposal
  Navigation is a very crucial aspect of autonomous vehicle ecosystem which heavily relies on collecting and processing large amounts of data in various states and taking a confident and safe decision to define the next vehicle maneuver. In this paper, we propos…
  arxiv Hemanth Kannamarlapudi, Sowmya Chintalapudi 2025-06-19T03:45:49Z 置信度 0.78 cs.ETcs.AIcs.ROquant-ph
• SpikingNav: Robust Embodied Navigation with Spiking Neural Policies
  Embodied navigation requires an agent to make sequential decisions from egocentric observations in a physical environment. Existing Artificial Neural Network (ANN)-based navigation models have achieved strong performance, yet they often rely on dense computati…
  arxiv Jiahong Zhang, Sijun Shen, Dehua Wu, Yifan Lin 等 2026-08-05T17:21:09Z 置信度 0.78 cs.RO
• NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance
  Learning to navigate in dynamic and complex open-world environments is a critical yet challenging capability for autonomous robots. Existing approaches often rely on cascaded modular frameworks, which require extensive hyperparameter tuning or learning from li…
  arxiv Wenzhe Cai, Jiaqi Peng, Yuqiang Yang, Yujian Zhang 等 2025-05-13T16:20:28Z 置信度 0.78 cs.RO
• On Inertial Navigation and Attitude Initialization in Polar Areas
  Inertial navigation and attitude initialization in polar areas become a hot topic in recent years in the navigation community, as the widely-used navigation mechanization of the local level frame encounters the inherent singularity when the latitude approaches…
  arxiv Yuanxin Wu, Chao He, Gang Liu 2019-03-28T09:50:47Z 置信度 0.78 cs.RO
• Sound Adversarial Audio-Visual Navigation
  Audio-visual navigation task requires an agent to find a sound source in a realistic, unmapped 3D environment by utilizing egocentric audio-visual observations. Existing audio-visual navigation works assume a clean environment that solely contains the target s…
  arxiv Yinfeng Yu, Wenbing Huang, Fuchun Sun, Changan Chen 等 2022-02-22T14:19:42Z 置信度 0.78 cs.SDcs.CVcs.ROeess.AS
• Semantic Audio-Visual Navigation
  Recent work on audio-visual navigation assumes a constantly-sounding target and restricts the role of audio to signaling the target's position. We introduce semantic audio-visual navigation, where objects in the environment make sounds consistent with their se…
  arxiv Changan Chen, Ziad Al-Halah, Kristen Grauman 2020-12-21T18:59:04Z 置信度 0.78 cs.CVcs.LGcs.ROcs.SDeess.AS
• OctoNav: Towards Generalist Embodied Navigation
  Embodied navigation stands as a foundation pillar within the broader pursuit of embodied AI. However, previous navigation research is divided into different tasks/capabilities, e.g., ObjNav, ImgNav and VLN, where they differ in task objectives and modalities, …
  arxiv Chen Gao, Liankai Jin, Xingyu Peng, Jiazhao Zhang 等 2025-06-11T15:15:17Z 置信度 0.78 cs.CVcs.AIcs.RO
• Embodied4C: Measuring What Matters for Embodied Vision-Language Navigation
  Vision-language navigation requires agents to reason and act under constraints of embodiment. While vision-language models (VLMs) demonstrate strong generalization, current benchmarks provide limited understanding of how embodiment -- i.e., the choice of physi…
  arxiv Tin Stribor Sohn, Maximilian Dillitzer, Jason J. Corso, Eric Sax 2025-12-19T19:47:55Z 置信度 0.78 cs.ROcs.CV
• Humanoid robot learning of complex behaviors with LLMs
  Learning complex behaviors by humanoid robots could be achieved with natural interactions aided by large language models.
  europepmc Amos Matsiko 2025 置信度 0.80
• Robot Learning from Demonstration
  crossref 2012-04-05T10:35:16Z 置信度 0.70
• Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives.
  europepmc 2019 置信度 0.80
• Robot Learning From Randomized Simulations: A Review.
  europepmc 2022 置信度 0.80
• Learning Neural Parsers with Deterministic Differentiable Imitation Learning
  We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions of their in…
  arxiv Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling, Kris M. Kitani 2018-06-20T16:15:54Z 置信度 0.78 cs.LGcs.AIcs.CVcs.ROstat.ML
• Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events
  This work develops a learning-based contact estimator for legged robots that bypasses the need for physical sensors and takes multi-modal proprioceptive sensory data as input. Unlike vision-based state estimators, proprioceptive state estimators are agnostic t…
  arxiv Tzu-Yuan Lin, Ray Zhang, Justin Yu, Maani Ghaffari 2021-06-29T20:36:07Z 置信度 0.78 cs.RO
• NOIR 2.0: Neural Signal Operated Intelligent Robots for Everyday Activities
  Neural Signal Operated Intelligent Robots (NOIR) system is a versatile brain-robot interface that allows humans to control robots for daily tasks using their brain signals. This interface utilizes electroencephalography (EEG) to translate human intentions rega…
  arxiv Tasha Kim, Yingke Wang, Hanvit Cho, Alex Hodges 2025-11-25T20:56:27Z 置信度 0.78 cs.ROcs.AIcs.HCcs.LGeess.SY
• R3M: A Universal Visual Representation for Robot Manipulation
  We study how visual representations pre-trained on diverse human video data can enable data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a visual representation using the Ego4D human video dataset using a combination of…
  arxiv Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn 等 2022-03-23T17:55:09Z 置信度 0.78 cs.ROcs.AIcs.CVcs.LG
• Alpha MAML: Adaptive Model-Agnostic Meta-Learning
  Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classificati…
  arxiv Harkirat Singh Behl, Atılım Güneş Baydin, Philip H. S. Torr 2019-05-17T18:45:25Z 置信度 0.78 cs.LGcs.AIstat.ML
• Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning
  Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted with motor control throughout thousands of years. Although compelling, the idea of…
  arxiv Kevin Sebastian Luck, Heni Ben Amor, Roberto Calandra 2019-11-15T19:01:21Z 置信度 0.78 cs.LGcs.AIcs.NEcs.ROstat.ML
• Generalist Robot Manipulation beyond Action Labeled Data
  Recent advances in generalist robot manipulation leverage pre-trained Vision-Language Models (VLMs) and large-scale robot demonstrations to tackle diverse tasks in a zero-shot manner. A key challenge remains: scaling high-quality, action-labeled robot demonstr…
  arxiv Alexander Spiridonov, Jan-Nico Zaech, Nikolay Nikolov, Luc Van Gool 等 2025-09-24T10:10:05Z 置信度 0.78 cs.RO
• PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards
  Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems. To address this, several prior approaches have used na…
  arxiv Prasoon Goyal, Scott Niekum, Raymond J. Mooney 2020-07-30T15:50:38Z 置信度 0.78 cs.LGcs.AIstat.ML
• Learning Generative Models across Incomparable Spaces
  Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), wh…
  arxiv Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka 2019-05-14T08:56:12Z 置信度 0.78 cs.LGstat.ML
• The Open Ant: A Robot Platform for Reinforcement Learning Research
  Reinforcement learning (RL) research has demonstrated success in both physical and simulated domains; however, the predominant methodology remains rooted in simulations. The predominance of simulations makes translating research to physical reality uncertain f…
  arxiv Elena Sorina Lupu, Patrick Spieler, Khurram Javed, Kris De Asis 等 2026-07-20T20:18:29Z 置信度 0.78 cs.ROcs.AIeess.SY
• Meta-Learning Probabilistic Inference For Prediction
  This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations…
  arxiv Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin 等 2018-05-24T22:08:27Z 置信度 0.78 stat.MLcs.LG
• Interactive Imitation Learning in State-Space
  Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available demonstration data. Interactive Imitation Learning techniques can improve the e…
  arxiv Snehal Jauhri, Carlos Celemin, Jens Kober 2020-08-02T17:23:54Z 置信度 0.78 cs.ROcs.LG
• Risk-Aware Active Inverse Reinforcement Learning
  Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize…
  arxiv Daniel S. Brown, Yuchen Cui, Scott Niekum 2019-01-08T05:23:03Z 置信度 0.78 cs.LGstat.ML
• Robot Parkour Learning
  Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments. Existing methods can generate either diverse but blind locomotion skills or vision-based but specialized skills by using refe…
  arxiv Ziwen Zhuang, Zipeng Fu, Jianren Wang, Christopher Atkeson 等 2023-09-11T17:59:17Z 置信度 0.78 cs.ROcs.AIcs.CVcs.LG
• Deep Reinforcement Learning for Decentralized Multi-Robot Control: A DQN Approach to Robustness and Information Integration
  The superiority of Multi-Robot Systems (MRS) in various complex environments is unquestionable. However, in complex situations such as search and rescue, environmental monitoring, and automated production, robots are often required to work collaboratively with…
  arxiv Bin Wu, C Steve Suh 2024-08-21T04:49:49Z 置信度 0.78 cs.ROcs.MA
• BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
  In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning. We approach the challenge from an imitation learning perspective, aiming to study how scaling …
  arxiv Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler 等 2022-02-04T07:30:48Z 置信度 0.78 cs.ROcs.LG
• Transfer Learning with Pre-trained Conditional Generative Models
  Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label spaces overlap, (ii) source datasets are available, and (iii) target network arc…
  arxiv Shin'ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai, Daiki Chijiwa 等 2022-04-27T10:36:32Z 置信度 0.78 cs.LGcs.AIstat.ML
• Federated Learning with Superquantile Aggregation for Heterogeneous Data
  We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach hinges upon a superquantile-based learning objective that captures the tail stat…
  arxiv Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaid Harchaoui 2021-12-17T11:00:23Z 置信度 0.78 cs.LGmath.OCstat.ML
• Motivating Physical Activity via Competitive Human-Robot Interaction
  This project aims to motivate research in competitive human-robot interaction by creating a robot competitor that can challenge human users in certain scenarios such as physical exercise and games. With this goal in mind, we introduce the Fencing Game, a human…
  arxiv Boling Yang, Golnaz Habibi, Patrick E. Lancaster, Byron Boots 等 2022-02-14T22:19:58Z 置信度 0.78 cs.ROcs.AIcs.HCcs.MA
• DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations
  An option is a short-term skill consisting of a control policy for a specified region of the state space, and a termination condition recognizing leaving that region. In prior work, we proposed an algorithm called Deep Discovery of Options (DDO) to discover op…
  arxiv Sanjay Krishnan, Roy Fox, Ion Stoica, Ken Goldberg 2017-10-15T23:51:37Z 置信度 0.78 cs.RO
• Empathic Robot for Group Learning: A Field Study
  This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies foster collaborative learning in a group context? (2) Can an empathic robot susta…
  arxiv Patricia Alves-Oliveira, Pedro Sequeira, Francisco S. Melo, Ginevra Castellano 等 2019-02-05T17:15:29Z 置信度 0.78 cs.HC
• Guiding Evolutionary Strategies by Differentiable Robot Simulators
  In recent years, Evolutionary Strategies were actively explored in robotic tasks for policy search as they provide a simpler alternative to reinforcement learning algorithms. However, this class of algorithms is often claimed to be extremely sample-inefficient…
  arxiv Vladislav Kurenkov, Bulat Maksudov 2021-10-01T14:20:00Z 置信度 0.78 cs.ROcs.AIcs.LGcs.NEeess.SY
• CLASS: Contrastive Learning via Action Sequence Supervision for Robot Manipulation
  Recent advances in Behavior Cloning (BC) have led to strong performance in robotic manipulation, driven by expressive models, sequence modeling of actions, and large-scale demonstration data. However, BC faces significant challenges when applied to heterogeneo…
  arxiv Sung-Wook Lee, Xuhui Kang, Brandon Yang, Yen-Ling Kuo 2025-08-03T05:37:25Z 置信度 0.78 cs.RO
• Affect-Driven Modelling of Robot Personality for Collaborative Human-Robot Interactions
  Collaborative interactions require social robots to adapt to the dynamics of human affective behaviour. Yet, current approaches for affective behaviour generation in robots focus on instantaneous perception to generate a one-to-one mapping between observed hum…
  arxiv Nikhil Churamani, Pablo Barros, Hatice Gunes, Stefan Wermter 2020-10-14T16:34:14Z 置信度 0.78 cs.ROcs.AI
• ChainerRL: A Deep Reinforcement Learning Library
  In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art re…
  arxiv Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa 2019-12-09T08:59:15Z 置信度 0.78 cs.LGcs.AIstat.ML
• AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers
  The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial to solve long-horizon tasks with sparse rewards. We propose to leverage an ense…
  arxiv Andrey Kurenkov, Ajay Mandlekar, Roberto Martin-Martin, Silvio Savarese 等 2019-09-09T19:38:25Z 置信度 0.78 cs.LGcs.AIstat.ML
• Learning Flat Latent Manifolds with VAEs
  Measuring the similarity between data points often requires domain knowledge, which can in parts be compensated by relying on unsupervised methods such as latent-variable models, where similarity/distance is estimated in a more compact latent space. Prevalent …
  arxiv Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer 等 2020-02-12T09:54:52Z 置信度 0.78 stat.MLcs.LG
• Learning a visuomotor controller for real world robotic grasping using simulated depth images
  We want to build robots that are useful in unstructured real world applications, such as doing work in the household. Grasping in particular is an important skill in this domain, yet it remains a challenge. One of the key hurdles is handling unexpected changes…
  arxiv Ulrich Viereck, Andreas ten Pas, Kate Saenko, Robert Platt 2017-06-14T19:50:09Z 置信度 0.78 cs.ROcs.AI
• Operator-valued Kernels for Learning from Functional Response Data
  In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function. We focus on the use of reproducing kernel H…
  arxiv Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu 等 2015-10-28T09:18:50Z 置信度 0.78 cs.LGstat.ML
• Robot Learning Using Learning Classifier Systems Approach
  crossref Suraiya Jabin 2012-03-29T08:15:43Z 置信度 0.70
• Robot-Aided Learning and r-Learning Services
  crossref Jeonghye Han 2012-03-23T19:39:19Z 置信度 0.70
• Introduction to Robot Learning
  crossref Jonathan H. Connell, Sridhar Mahadevan 2011-06-20T01:47:34Z 置信度 0.70
• Learning enabled intelligent robot new era
  crossref Hesheng Wang, Chenguang Yang 2024-07-29T02:47:57Z 置信度 0.70
• Robot Learning
  crossref 2012-02-07T13:44:39Z 置信度 0.70
• IEEE Transactions on Robot Learning
  crossref 2026-03-31T19:54:59Z 置信度 0.70
• Multi-robot Formation Control with Kernel-based Reinforcement Learning
  crossref Jun WU, Xin XU, Chuanqiang LIAN, Yan HUANG 2011-09-27T08:49:14Z 置信度 0.70
• Robot Learning of Domain Specific Knowledge from Natural Language Sources
  crossref Ines eh, Sandi Pohorec, Marjan Mernik, Milan Zorm 2012-03-29T08:15:43Z 置信度 0.70
• Multi-robot Concurrent Learning
  crossref 2012-04-05T10:39:17Z 置信度 0.70
• Robot Learning from Feedback
  crossref 2012-04-05T06:35:16Z 置信度 0.70
• Combination of decision making and machine learning for improvement of robot learning for water analysis
  crossref Taraneh Javanbakht, Arbnor Pajaziti, Shaban Buza 2025-08-21T11:30:57Z 置信度 0.70
• Efficient Robot Skill Learning: Grounded Simulation Learning and Imitation Learning from Observation
  crossref Peter Stone 2021-05-18T16:37:41Z 置信度 0.70
• A Bionic Learning Algorithm Based on Skinner's Operant Conditioning and Control of Robot
  crossref Hongge REN, Xiaogang RUAN 2010-06-04T06:49:20Z 置信度 0.70
• The Semantic Hierarchy in Robot Learning
  crossref Benjamin Kuipers, Richard Froom, Wan-Yik Lee, David Pierce 2011-06-20T01:47:34Z 置信度 0.70
• Robot Learning Via Human–Robot Interaction: The Future of Computer Programming
  crossref 2012-04-05T10:35:16Z 置信度 0.70
• Robot ℋ2 Neural Control Using Reinforcement Learning
  crossref 2021-10-02T13:40:50Z 置信度 0.70
• Reinforcement Learning for Robot Position/Force Control
  crossref 2021-10-02T13:40:50Z 置信度 0.70
• Motor Schemas in Robot Learning
  crossref 2012-04-05T10:35:16Z 置信度 0.70
• Inter-Robot Learning in Multi-Robot System
  crossref Abhijeet Ravankar, Ankit A. Ravankar, Yukinori Kobayashi, Takanori Emaru 2017-11-24T22:33:18Z 置信度 0.70
• Robot Control in Worst‐Case Uncertainty Using Reinforcement Learning
  crossref 2021-10-02T13:40:50Z 置信度 0.70
• Robot Autonomy, Decision-Making, and Learning
  crossref 2020-07-09T17:45:23Z 置信度 0.70
• Novel Framework of Robot Force Control Using Reinforcement Learning
  crossref Byungchan Kim, Shinsuk Park 2012-03-29T07:45:26Z 置信度 0.70
• Task learning of teleoperation robot systems
  crossref Chenguang Yang, Jing Luo, Ning Wang 2023-04-21T17:14:50Z 置信度 0.70
• Robot Learning Via Human–Robot Interaction: The Future of Computer Programming
  crossref Qiong Liu, Chunyuan Liao 2012-01-06T12:11:29Z 置信度 0.70
• Multi-Robot Systems Control Implementation
  crossref Jose Manuel, Ekaitz Zulueta, Borja Fernndez, Manuel Gr 2012-03-29T08:15:43Z 置信度 0.70
• Reinforcement Learning for Mobile Robot Perceptual Learning
  crossref Xiaochun Wang, Xiali Wang, Don Mitchell Wilkes 2019-08-12T09:29:32Z 置信度 0.70
• Front Cover
  crossref 2026-03-31T19:52:07Z 置信度 0.70
• Q-Learning Adjusted Bio-Inspired Multi-Robot Coordination
  crossref Yan Meng 2012-03-23T19:28:04Z 置信度 0.70
• Lifelong, learning-augmented robot navigation
  Simultaneous localization and mapping (SLAM) is the process by which a robot constructs a global model of an environment from local observations of it; this is a fundamental perceptual capability supporting planning, navigation, and control. We are interested …
  crossref Kevin J. Doherty 2023-02-02T16:13:38Z 置信度 0.70
• Towards Robot Skill Learning
  crossref 2020-05-19T21:12:07Z 置信度 0.70
• Sensorimotor learning for ground robot navigation
  crossref Lei Tai 2020-04-01T05:09:43Z 置信度 0.70
• Virtual Robot Climbing using Reinforcement Learning
  crossref Ujjawal Garg 2019-04-18T14:04:46Z 置信度 0.70
• Visual Learning for Mobile Robot Localisation
  crossref David Prasser 2024-04-11T00:09:46Z 置信度 0.70
• Build an Emoting Robot Using Arduino and Machine Learning
  crossref Julia Makivic 2020-08-27T09:05:00Z 置信度 0.70
• Reinforcement learning for robot assembly
  crossref Quoc Nghia Vuong 2024-05-03T02:43:29Z 置信度 0.70
• Robot Learning from Human: Hierarchical Interactions
  crossref Changliu Liu, Te Tang, Hsien-Chung Lin, Masayoshi Tomizuka 2020-01-08T15:57:38Z 置信度 0.70
• Deep Reinforcement Learning and Learning from Demonstrations for Robot Manipulators
  Apprentissage profond par Renforcement et Démonstrations, pour le comportement de robots manipulateurs Malgré leur grand succès, les algorithmes d'apprentissage par renforcement doivent encore devenir plus efficaces en termes d'échantillons, en particulier pou…
  crossref Jesús Bujalance martin 2026-04-08T18:11:27Z 置信度 0.70
• Machine Learning
  crossref 2014-11-13T17:10:55Z 置信度 0.70
• Mobile-Robot Navigation
  crossref Wyatt S. Newman 2018-12-15T08:07:09Z 置信度 0.70
• Robot learning-Beyond imitation.
  europepmc 2019 置信度 0.80
• Multi-task learning for underwater robot via progressive neural network
  crossref Xingwei Pan, Song Xiang, Shilong Niu, Zheng Fang 等 2025-11-05T00:30:07Z 置信度 0.70
• PASSIVITY OF ROBOT DYNAMICS IN LEARNING CONTROL
  crossref S. Arimoto, T. Naniwa 2014-07-01T09:09:40Z 置信度 0.70
• Robot Arm Kinematics
  crossref Wyatt S. Newman 2018-12-15T13:07:09Z 置信度 0.70
• Robot assembly strategy optimization based on embodied skill learning
  crossref Fengming Li, Hui Qi, Ligang Jin, Xiaoqing Yao 等 2026-05-21T12:37:48Z 置信度 0.70
• Robot Reinforcement Learning
  crossref 2012-01-06T12:11:29Z 置信度 0.70