Embodied Question Answering: A goal-driven approach to autonomous agents

Introduced Embodied Question Answering (EmbodiedQA), a new AI task requiring agents to combine perception, navigation, and communication to answer questions in virtual environments. Developed House3D, a collection of 45,000 simulated indoor environments for training and testing these agents. Proposed a modular navigation approach splitting tasks between a planner and controller. Explored learning capabilities including active perception, commonsense reasoning, language grounding, and credit assignment using reinforcement learning, imitation learning, and reward shaping. Released EmbodiedQA data and House3D as open source.