Imitation learning has traditionally been applied to learn a single task from demonstrations thereof. The requirement of structured and isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real-world scenarios, where robots have to be able to execute a multitude of tasks. In this paper, we propose a multi-modal imitation learning framework that is able to segment and imitate skills from unlabelled and unstructured demonstrations by learning skill segmentation and imitation learning jointly. The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi-modal policy. The video of our experiments is available at this http URL
The works that are most closely related to this paper are @cite_20 , @cite_19 and @cite_10 . @cite_19 , the authors show a method that is able to learn disentangled representations and apply it to the problem of image generation. In this work, we provide an alternative derivation of our method that extends their work and applies it to multi-modal policies. @cite_20 , the authors present an imitation learning GAN approach that serves as a basis for the development of our method. We provide an extensive evaluation of the hereby presented approach compared to the work in @cite_20 , which shows that our method, as opposed to @cite_20 , can handle unstructured demonstrations of different skills. A concurrent work @cite_10 introduces a method similar to ours and applies it to detecting driving styles from unlabelled human data.
@cite_19 @cite_10 @cite_20
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While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity. Source code for official implementation is publicly available this https URL Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.