Recent Advances in Robot Learning
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The combination of artificial intelligence AI and industrial or collaborative robotics has the potential to change the world. AI unlocks entirely new capabilities for robots, which, without AI, are rigid and unresponsive to the world around them.
- Recent Advances on Vision-Based Robot Learning by Demonstration | Bentham Science.
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- The next big breakthrough in robotics.
- Types of Industrial and Collaborative Robotic Learning.
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- UC Berkeley Robot Learning Lab: Home.
- Download Recent Advances In Robot Learning.
The potential for disruption in the industrial sector is high. Despite the fact that industrial processes are already highly automated, there are still plenty of ways in which industrial robots can be improved with the addition of AI. The three main types of robotic learning involve supervised learning, unsupervised learning and reinforcement learning.
Each varies in complexity, but the purpose is the same in all three learning methods.
Supervised learning is simply pattern recognition — feeding a robot data that it is then supposed to learn whatever pattern is intended by the instructors. Objective: Providing the references for researchers who work in related fields by reviewing recent advances of vision-based LbD. Methods: This paper reviews the latest patents and current representative articles related to visionbased LbD. The key methods of these references are introduced in the aspects of algorithms, innovations and principles.
Types of Industrial and Collaborative Robotic Learning
Results: The researches related to vision-based LbD in the last 5 years are classified, the advantages of different algorithms in these patents and articles are introduced and analyzed, the future developments and potential problems in this field are discussed. Conclusion: The main advantage of vision-based LbD is to allow users training robots for new tasks by the demonstration under the vision sensor without programming control. So, vision-based LbD provides an intuitive manner of robot learning by demonstration to solve the problem of human-robot interaction.
Further improvement is required in the following aspects: Algorithm innovation, multiple demonstrations, many definitions of human action and so on.
AI & Robotics: 3 Trends to Keep an Eye On
More patents on vision-based LbD should be invented. These characteristics present challenges and constraints to the learning system.
Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution.
Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning Volume 23, Numbers 2 and 3. Product details Format Hardback pages Dimensions x x Other books in this series.
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Recent Advances on Vision-Based Robot Learning by Demonstration
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