Robots operating in human spaces must be able to engage in natural language interaction,
both understanding and executing instructions, and using conversation to resolve ambiguity …

We study the problem of learning a range of vision-based manipulation tasks from a large
offline dataset of robot interaction. In order to accomplish this, humans need easy and …

Abstract Purpose of Review This review provides an overview of the motivation, challenges,
state-of-the-art, and recent research for human-robot interaction (HRI) in space. For context …

With the recent development of deep learning, AI models are widely used in various
domains. AI models show good performance for definite tasks such as image classification …

Learning policies that effectively utilize language instructions in complex, multi-task
environments is an important problem in imitation learning. While it is possible to condition …

Reward learning is a fundamental problem in human-robot interaction to have robots that
operate in alignment with what their human user wants. Many preference-based learning …

With increasing autonomy, robots like drones are increasingly accessible to untrained users.
Most users control drones using a low-level interface, such as a radio-controlled (RC) …

Layered architectures have been widely used in robot systems. The majority of them
implement planning and execution functions in separate layers. However, there still lacks a …

In a rapidly evolving digital landscape autonomous tools and robots are becoming
commonplace. Recognizing the significance of this development, this paper explores the …

Embodiment is an important characteristic for all intelligent agents, while existing scene
description tasks mainly focus on analyzing images passively and the semantic …