A Continual Learning Approach for Embodied Question Answering with Generative Adversarial Imitation Learning

Embodied Question Answering (EQA) is a task in artificial intelligence where an intelligent agent is required to answer questions about its environment. For example, to answer a question such as "Is the TV on or off?", the agent must navigate to the room with the TV and answer with either "On." or "Off." after recognizing the status. Unlike traditional question-answering systems that rely solely on text or static images, EQA involves agents that can move through a physical or simulated space, interact with the environment, and gather information to respond accurately. The agent must interpret both visual and linguistic inputs, navigate the environment, and complete tasks or locate objects based on the user’s questions. However, in the real world, the agent always faces unseen environments (i.e. different people’s houses), which makes the pre-trained model fail. Meanwhile, re-training in an unseen environment can cause high costs. Therefore, it is significant for the agent to learn continually by itself to cope with the challenges of unseen environments. In this work, we proposed a continual learning method based on generative adversarial imitation learning and self-supervision to support the agent when facing unseen environments. Besides, we designed a policy generator and policy quality discriminator to generate action policy sequences and evaluate the quality of the policy, respectively. Extensive experiments on the MP3D-EQA dataset demonstrate that our method reaches state-of-the-art performance.