Revisiting EmbodiedQA: A Simple Baseline and Beyond.

Area of Science:

• Artificial Intelligence
• Robotics
• Computer Vision

Background:

• Embodied Question Answering (EmbodiedQA) involves agents interacting with environments to answer questions.
• Current EmbodiedQA approaches face challenges, particularly in navigation, indicating limitations in contemporary methods.

Purpose of the Study:

• To empirically study the challenges in EmbodiedQA.
• To introduce a simple yet effective baseline for EmbodiedQA.
• To propose a new, more practical setting for EmbodiedQA that facilitates model adaptation to new environments.

Main Methods:

• Developed a straightforward baseline agent for EmbodiedQA tasks.
• Introduced a novel setting where agents adapt to new environments using a distillation network before answering questions.
• Randomly placed objects in new environments to facilitate adaptation.

Main Results:

• The proposed baseline achieved competitive performance against state-of-the-art methods in the standard EmbodiedQA setting.
• In the new adaptation setting, agents demonstrated a notable improvement in navigation capabilities.
• The distillation network effectively retained generalization ability during adaptation.

Conclusions:

• The proposed baseline offers a strong foundation for EmbodiedQA research.
• The new adaptation setting significantly enhances agent performance and practicality.
• Further research into agent adaptation strategies can unlock greater potential in EmbodiedQA.