Robotic systems need to operate in open and complex environments where complete knowledge of the world is impossible. This talk explores how Multimodal Large Language Models ((M)LLMs) can support robotic reasoning under conditions of uncertainty and also addresses the challenges posed by uncontrolled actors such as humans.
We will push beyond the direct application of MLLMs by introducing techniques to control hallucinations and ensure robust reasoning.
These techniques include integrating formal methodologies and neurosymbolic approaches to craft more reliable and adaptive reasoning processes for real-world robotic systems.
