Robustness of joint MLLM–WM embodied systems under sensor noise and partial observability

Ensure robustness of joint multimodal large language model–world model (MLLM–WM) driven embodied AI architectures against sensor noise and partial observability by developing methods that maintain reliable performance in dynamic, real-world environments.

Background

In discussing the proposed joint MLLM–WM architecture, the authors enumerate key challenges, including synchronization, semantic-physical alignment, memory management, and data requirements. They explicitly state that ensuring robustness to sensor noise and partial observability remains unsolved.

This open problem underscores the current inability of joint semantic-physical systems to maintain reliable behavior when sensors are noisy or the environment is only partially observable, which is critical for deployment in dynamic, real-world scenarios.