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Imitation Learning of Robot Policies by Combining Language, Vision, and Demonstration

In this work we propose a novel end-to-end imitation learning approach that combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn is used to synthesize specific motion controllers at run-time. This multimodal approach enables generalization to a wide variety of environmental conditions and allows an end-user…

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Neural Policy Translation for Robot Control

Teaching new skills to robots is usually a tedious process that requires expert knowledge and a substantial amount of time, depending on the complexity of the new task. Especially when used for imitation learning, rapid and intuitive ways of teaching novel tasks are needed. In this work, we outline Neural Policy Translation (NPT) – a…

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Towards Semantic Policies for Human-Robot Collaboration

As the application domain of robots moves closer to our daily lives, algorithms and methods are needed to ensure safe and meaningful human-machine interaction. Robots need to be able to understand human body movements, as well as the semantic meaning of these actions. To overcome this challenge, this research aims to create novel ways of…

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Speech Enhanced Imitation Learning and Task Abstraction for Human-Robot Interaction

In this short paper, we show how to learn interaction primitives and networks from long interactions by taking advantage of language and speech markers. The speech markers are obtained from free speech that accompanies the demonstration. We perform experiments to show the value of using speech markers for learning interaction primitives.

Deep Predictive Models for Active Slip Control

We discuss a machine learning methodology for actively controlling slip, in order to increase robot dexterity. Leveraging recent insights in Deep Learning, we propose a Deep Predictive Model that uses tactile sensor information to reason about slip and its future influence on the manipulated object. We show in a set of experiments that this approach…

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