With robotics rapidly advancing, more effective human–robot interaction is increasingly
needed to realize the full potential of robots for society. While spoken language must be part …

Collaborative tasks often begin with partial task knowledge and incomplete initial plans from
each partner. To complete these tasks, agents need to engage in situated communication …

Intelligent agents that are confronted with novel concepts in situated environments will need
to ask their human teammates questions to learn about the physical world. To better …

Interactive Task Learning (ITL) is an emerging research agenda that studies the design of
complex intelligent robots that can acquire new knowledge through natural human teacher …

With robotics rapidly advancing, more effective human-robot interaction is increasingly
needed to realize the full potential of robots for society. While spoken language must be part …

Dialogue agents that interact with humans in situated environments need to manage
referential ambiguity across multiple modalities and ask for help as needed. However, it is …

In the real world, autonomous driving agents navigate in highly dynamic environments full of
unexpected situations where pre-trained models are unreliable. In these situations, what is …

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal
collection of human-robot dialogue in the task domain of collaborative exploration. The …

We analyze the use and interpretation of modal expressions in a corpus of situated human-
robot dialogue and ask how to effectively represent these expressions for automatic …

Interactive Task Learning (ITL) is an approach to designing robots that can learn tasks on
the fly from human instruction and demonstration in a shared environment. ITL approaches …