cuticle
发表于 2025-3-23 10:10:07
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概观
发表于 2025-3-23 17:01:04
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改正
发表于 2025-3-23 20:10:52
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canonical
发表于 2025-3-23 23:23:11
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dermatomyositis
发表于 2025-3-24 04:18:47
Introduction,e of this technique is that these modules must be manually developed, arranged, and tuned for each task. Therefore, engineering these systems is labor-intensive and requires expert knowledge. For more complex tasks, unstructured environments, and unstructured observations, the associated complexity
情节剧
发表于 2025-3-24 09:17:06
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Gorilla
发表于 2025-3-24 13:50:11
Book 2023ules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insight
Asseverate
发表于 2025-3-24 15:24:35
1610-7438 ing or wanting to learn more on robot learning with inductiv.One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relie
Contort
发表于 2025-3-24 20:10:16
Conclusion, main take-away of this book is that one can use deep networks in more creative ways than naive input-output mappings for learning dynamics models or policies. In the following, we summarize the contributions of the three chapters and discuss the open challenges of the presented algorithms.
睨视
发表于 2025-3-25 02:19:20
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