Introspection for Long-Horizon Robot Planning under Uncertainty

Abstract

The next generation of household service robots must not only perform day-to-day tasks effectively, but also learn and improve over time by continuously evaluating their own performance. My research focuses on introspection as a technique that enables a robot to self-assess its behavior while performing long-horizon tasks in environments that may not be fully known. We show that such introspection enables improved performance in tasks such as navigation in unknown environments, deployment-time learning and adaptation, and LLM-informed object search.