Probabilistic model-based imitation learning
نویسندگان
چکیده
منابع مشابه
Probabilistic model-based imitation learning
Efficient skill acquisition is crucial for creating versatile robots. One intuitive way to teach a robot new tricks is to demonstrate a task and enable the robot to imitate the demonstrated behavior. This approach is known as imitation learning. Classical methods of imitation learning, such as inverse reinforcement learning or behavioral cloning, suffer substantially from the correspondence pro...
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Humans and animals use imitation as a mechanism for acquiring knowledge. Recently, several algorithms and models have been proposed for imitation learning in robots and humans. However, few proposals offer a framework for imitation learning in a stochastic environment where the imitator must learn and act under realtime performance constraints. We present a probabilistic framework for imitation...
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Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to maintain an oracle D that discriminates between the expert’s data distribution and that of the generative model G. The generative model is trained to capture the expert’s distribution by maximizing the probability of ...
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Learning by imitation represents an important mechanism for rapid acquisition of new behaviors in humans and robots. A critical requirement for learning by imitation is the ability to handle uncertainty arising from the observation process as well as the imitator’s own dynamics and interactions with the environment. In this paper, we present a new probabilistic method for inferring imitative ac...
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ژورنال
عنوان ژورنال: Adaptive Behavior
سال: 2013
ISSN: 1059-7123,1741-2633
DOI: 10.1177/1059712313491614