ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation

Technical Analysis Navigating and understanding complex environments over extended periods of time is a significant challenge for robots. To address this problem, we introduce “Retrieval-augmented Memory for Embodied Robots”, a system designed for long-horizon video question answering for robot navigation. High-level Analogy: Imagine a robot as a diligent explorer keeping a vast, ever-growing diary of everything it sees and experiences during its long journeys. Traditional robots are like explorers who can only remember the details from the last few pages of their diary. If you ask them about something from a week ago, they get overwhelmed trying to read through every single page or simply forget. ReMEmbR gives the robot a special kind of 'smart diary' and an incredibly efficient 'personal assistant.' Instead of writing down every single detail from its continuous video feed, the robot's brain (the memory building phase) watches short video snippets (like 3-second clips) and quickly summarizes what happened, where it was, and when. These summaries are then instantly stored in a highly organized, super-fast searchable archive (the vector database). When you ask the robot a complex question like, 'Where did you last see the red ball, and when was that?', the robot's personal assistant (the LLM-agent) doesn't start reading from page one. Instead, it intelligently thinks, 'Okay, I need to find 'red ball' and its location and time.' It uses its powerful search tools to quickly…