Due to burdensome data requirements, learning from demonstration often falls short of its promise to allow users to quickly and naturally program robots. Demonstrations are inherently ambiguous and incomplete, making correct generalization to unseen situations difficult without a large number of demonstrations in varying conditions. By contrast, humans are often able to learn complex tasks from a single demonstration (typically observations without action labels) by leveraging context learned over a lifetime. Inspired by this capability, our goal is to enable robots to perform one-shot learning of multi-step tasks from observation by leveraging auxiliary video data as context. Our primary contribution is a novel system that achieves this goal by: (1) using a single user-segmented demonstration to define the primitive actions that comprise a task, (2) localizing additional examples of these actions in unsegmented auxiliary videos via a metalearningbased approach, (3) using these additional examples to learn a reward function for each action, and (4) performing reinforcement learning on top of the inferred reward functions to learn action policies that can be combined to accomplish the task. We empirically demonstrate that a robot can learn multistep tasks more effectively when provided auxiliary video, and that performance greatly improves when localizing individual actions, compared to learning from unsegmented videos.

We propose a novel convolutional network architecture that abstracts and differentiates the categories based on a given class hierarchy. We exploit grouped and discriminative information provided by the taxonomy, by focusing on the general and specific components that comprise each category, through the min-and difference-pooling operations. Without using any additional parameters or substantial increase in time complexity, our model is able to learn the features that are discriminative for classifying often confused sub-classes belonging to the same superclass, and thus improve the overall classification performance. We validate our method on CIFAR-100, Places-205, and ImageNet Animal datasets, on which our model obtains significant improvements over the base convolutional networks.

We propose a novel reinforcement learning framework that performs self-supervised online reward shaping, yielding faster, sample efficient performance in sparse reward environments. The proposed framework alternates between updating a policy and inferring a reward function. While the policy update is done with the inferred, potentially dense reward function, the original sparse reward is used to provide a self-supervisory signal for the reward update by serving as an ordering over the observed trajectories. The proposed framework is based on the theory that altering the reward function does not affect the optimal policy of the original MDP as long as we maintain certain relations between the altered and the original reward. We name the proposed framework ClAssification-based REward Shaping (CaReS), since we learn the altered reward in a self-supervised manner using classifier based reward inference. Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than the state-of-the-art baseline, but also achieves a similar sample efficiency to MDPs that use hand-designed dense reward functions.

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