There has been an emerging paradigm shift from the era of “internet AI” to “embodied AI,”
where AI algorithms and agents no longer learn from datasets of images, videos or text …

We consider the problem of Vision-and-Language Navigation (VLN). The majority of current
methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or …

This work focuses on object goal visual navigation, aiming at finding the location of an object
from a given class, where in each step the agent is provided with an egocentric RGB image …

Object goal navigation (ObjectNav) in unseen environments is a fundamental task for
Embodied AI. Agents in existing works learn ObjectNav policies based on 2D maps, scene …

We study the task of semantic mapping–specifically, an embodied agent (a robot or an
egocentric AI assistant) is given a tour of a new environment and asked to build an …

We consider the problems of exploration and pointgoal navigation in previously unseen
environments, where the spatial complexity of indoor scenes and partial observability …

Embodied question answering (EQA) is a recently emerged research field in which an agent
is asked to answer the user's questions by exploring the environment and collecting visual …

The capacity to extract information and comprehend an unseen environment is critical for
mobile robots to navigate. Few surveys has mentioned the combinatorial-non-optimality …

The task of robot navigation and question answering, which is also known as Embodied
Question Answering (EQA), places its emphasis on empowering agents to actively explore …

Embodied Question Answering (EQA) is a newly defined research area where an agent is
required to answer the user's questions by exploring the real-world environment. It has …