標(biāo)題: Titlebook: Robot Learning from Human Teachers; Sonia Chernova,Andrea L. Thomaz Book 2014 Springer Nature Switzerland AG 2014 [打印本頁] 作者: DUCT 時間: 2025-3-21 18:46
書目名稱Robot Learning from Human Teachers影響因子(影響力)
書目名稱Robot Learning from Human Teachers影響因子(影響力)學(xué)科排名
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書目名稱Robot Learning from Human Teachers網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Robot Learning from Human Teachers被引頻次
書目名稱Robot Learning from Human Teachers被引頻次學(xué)科排名
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書目名稱Robot Learning from Human Teachers讀者反饋
書目名稱Robot Learning from Human Teachers讀者反饋學(xué)科排名
作者: Mets552 時間: 2025-3-21 23:08
Book 2014social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstra作者: resistant 時間: 2025-3-22 01:40 作者: 報復(fù) 時間: 2025-3-22 07:35 作者: Dealing 時間: 2025-3-22 10:04 作者: 人類 時間: 2025-3-22 16:53 作者: mitral-valve 時間: 2025-3-22 18:39
Robot Learning from Human Teachers978-3-031-01570-0Series ISSN 1939-4608 Series E-ISSN 1939-4616 作者: 尋找 時間: 2025-3-23 01:06
Refining a Learned Task,rner’s exploration. In general, this is a complex process where the teacher dynamically adjusts their support based on the learners demonstrated skill level. The learner, in turn, helps the instructor by making their learning process transparent through communicative acts, and by demonstrating their current knowledge and mastery of the task.作者: 喃喃而言 時間: 2025-3-23 05:01
Book 2014 into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts作者: 駁船 時間: 2025-3-23 08:35
1939-4608 has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not roboti作者: Graduated 時間: 2025-3-23 12:16 作者: 線 時間: 2025-3-23 14:18
Introduction, the real world. Today, and for the foreseeable future, it is not possible to go to a store and bring home a robot that will clean your house, cook your breakfast, and do your laundry. These everyday tasks, while seemingly simple, contain many variations and complexities that pose insurmountable challenges for today’s machine learning algorithms.作者: exostosis 時間: 2025-3-23 19:57 作者: Limpid 時間: 2025-3-24 01:43
Learning Low-Level Motion Trajectories, . in which they would be used (covered in Chapter 5). In the literature there are several different names given to this class of “l(fā)ow-level” action learning, thus in this chapter we use the terms . and . interchangeably.作者: GIBE 時間: 2025-3-24 02:34
Learning High-Level Tasks,ning a reactive task policy representing a functional mapping of states to actions, learning a task plan, and learning the task objectives. We go on to discuss the role that feature selection, reference frame identification and object affordances play in the learning process.作者: Distribution 時間: 2025-3-24 08:32 作者: 主講人 時間: 2025-3-24 12:56
Human Social Learning, process. Although robots can also learn from observing demonstrations not directed at them, albeit less efficiently, the scenario we address here is primarily the one where a person is explicitly trying to teach the robot something in particular.作者: affect 時間: 2025-3-24 18:22 作者: 你敢命令 時間: 2025-3-24 20:12
Learning Low-Level Motion Trajectories,algorithm can be designed to work with. We now turn our attention to the wide range of algorithms for building skill and task models from demonstration data. In this chapter we focus on approaches that learn new motions or primitive actions. The motivation behind learning new motions is typically th作者: Systemic 時間: 2025-3-25 02:47
Learning High-Level Tasks, (Figure 5.1). While the line between high-level and low-level learning is not concrete, the distinction we make here is that techniques in this chapter assume the existence of a discrete set of action primitives that can be combined to perform a more complex behavior. As in the previous chapter, we作者: 帶傷害 時間: 2025-3-25 05:41 作者: 神刊 時間: 2025-3-25 10:03 作者: LAPSE 時間: 2025-3-25 14:02
Future Challenges and Opportunities,h of the chapters of this book has offered some suggestions for future research in the respective topics; in this chapter we discuss three closely inter-related research directions that we consider major challenges for the field. We also provide the interested reader with a list of additional readin作者: 得罪人 時間: 2025-3-25 16:41
agnostik.Erg?nzte Beitr?ge zu den Grundlagen, Kommunikation Das Buch stellt das von Brigitte Scharb entwickelte Pflegekonzept zur Befriedigung psychosozialer Grundbedürfnisse desorientierter, hochbetagter Personen vor, mit dem Ziel vorhandene Kompetenzen der Betroffenen zu f?rdern bzw. zu bewahren. 作者: Palatial 時間: 2025-3-25 21:50 作者: 必死 時間: 2025-3-26 01:36
Michel Kieffer,Isabelle Braems,éric Walter,Luc Jaulinayful and the emotional and creative dimensions of learning.The main purpose of this book is to take a closer look at how students and teachers in educational institutions apply the innovative, the playful and the emotional and creative dimensions of learning. With this contribution, the authors aim作者: 新奇 時間: 2025-3-26 06:00
D. W. Walkernicopathologic features of well-known viral pathogens and em.Viruses that primarily target the lung are very significant causes of death and in the past decade have been responsible for major outbreaks of severe adult respiratory distress syndrome and H1N1 influenza. This book is distinctive in that作者: 喚醒 時間: 2025-3-26 11:35
Ludwig J?gerider "New Scientific and Technical Aspects of Verification of the Biological and Toxin Weapons Convention". In many ways the meeting was ahead of its time. The Ad Hoc Group was only then about to move to the discussion of a rolling text of the Protocol to the Biological and Toxin Weapons Convention 作者: deface 時間: 2025-3-26 16:05
Experimenting with Deduction Modulo,es rewriting terms and propositions. In CSL?2010, LNCS?6247, p.155–169, we gave theoretical justifications why it is possible to embed a proof search method based on deduction modulo, namely Ordered Polarized Resolution Modulo, into an existing prover. Here, we describe the implementation of these i作者: FLINT 時間: 2025-3-26 19:58 作者: GOUGE 時間: 2025-3-26 22:12
Global Standards and Local Developmentin developing countries are commonly challenged by the proliferation of multiple and parallel information systems. Investments are made, but not in a coordinated manner. Based on a case study of OpenHIE, a global community of practice supporting the development of ICT standards within health, and th作者: 無孔 時間: 2025-3-27 03:54 作者: incredulity 時間: 2025-3-27 07:04 作者: Optimum 時間: 2025-3-27 10:22 作者: Hemiplegia 時間: 2025-3-27 17:10
Analysis of Waterfront Excavation Adjacent to Pile-supported Wharves,bal bending stiffness and reduce the lateral soil pressure. Therefore, the maximum lateral displacement of the composite steel sheet pile wall is only one-third that of the diaphragm wall and the safety factor against basal heave is higher than the diaphragm wall.作者: Inelasticity 時間: 2025-3-27 20:04 作者: Anthem 時間: 2025-3-27 21:58 作者: MEAN 時間: 2025-3-28 04:08 作者: 不合 時間: 2025-3-28 08:24
From a Nascent to a Mature Regional Innovation System: What Drives the Transition?e and a Consensus Space. The spaces co-evolve in a multitude of ways and directions as a non-linear process and provide a detailed view of regional actors, knowledge flows and interactions between them, and the resources available, in view of identifying existing blockages or gaps and formulating po作者: 點燃 時間: 2025-3-28 12:23 作者: 向外 時間: 2025-3-28 15:22 作者: PALSY 時間: 2025-3-28 19:50 作者: micturition 時間: 2025-3-29 01:15
Solving Partial Differential Equations Using Point-Based Neural Networksodel on different types of PDEs. The numerical results verify that our model possesses a higher precision and a faster inference speed compared with the existing models for data of unstructured meshes; in addition, our model has competitive performance compared with the existing works dealing with d
