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This paper describes an approach for a robotic arm to learn new actions through dialogue in a simplified blocks world. In particular, we have developed a threetier action knowledge representation that on one hand, supports the connection between symbolic representations of language and continuous sensorimotor representations of the robot; and on the other hand, supports the application of existing planning algorithms to address novel situations. Our empirical studies have shown that, based on this representation the robot was able to learn and execute basic actions in the blocks world. When a human is engaged in a dialogue to teach the robot new actions, step-by-step instructions lead to better learning performance compared to one-shot instructions.

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Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.This paper explores data augmentation-a technique particularly suitable for training with limited data-for this few-shot, highlymulticlass text classification setting. On four diverse text classification tasks, we find that common data augmentation techniques can improve the performance of triplet networks by up to 3.0% on average.To further boost performance, we present a simple training strategy called curriculum data augmentation, which leverages curriculum learning by first training on only original examples and then introducing augmented data as training progresses. We explore a twostage and a gradual schedule, and find that, compared with standard single-stage training, curriculum data augmentation trains faster, improves performance, and remains robust to high amounts of noising from augmentation.

Robots often have limited knowledge and need to continuously acquire new knowledge and skills in order to collaborate with its human partners. To address this issue, this paper describes an approach which allows human partners to teach a robot (i.e., a robotic arm) new high-level actions through natural language instructions. In particular, built upon the traditional planning framework, we propose a representation of high-level actions that only consists of the desired goal states rather than step-by-step operations (although these operations may be specified by the human in their instructions). Our empirical results have shown that, given this representation, the robot can reply on automated planning and immediately apply the newly learned action knowledge to perform actions under novel situations.

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