From Perceptions to Meaning: Multimodal and Contrastive Machine Learning to Ground Human Intentions and Language

The overarching theme of this thesis is making sense of senses—a journey from perception to meaning by teaching machines to uncover the latent connections between natural language and the physical world as perceived through multiple sensor modalities. Learning these connections is referred to as multimodal grounded language learning, and it allows AI agents and robots to interact more intuitively with their environments and communicate naturally with humans. By understanding how language aligns with the physical world, AI systems can go beyond recognizing objects and begin to infer high-level human intentions behind tasks—an essential step for building truly intelligent, interactive systems. The growing demand for personal robots as caretakers is just one example that illustrates the real-world importance of this goal. Training AI to interact effectively in such situations requires an understanding of objects through multiple sensor modalities and the language humans use to describe them. To facilitate this, I contributed to the development of a multimodal dataset of everyday objects, and proposed approaches to multimodal grounding of language, including Extended Multimodal Alignment (EMMA), a model capable of integrating any number of modalities while remaining robust to sensor failures. EMMA not only sets a new state-of-the-art by learning effectively from low amounts of data but also converges to optimal performance twice as fast. However, understanding objects is only part of the challenge—humans often communicate abstractly, omitting steps to perform a task or using indirect speech acts. For instance, when someone tells a robot, ‘the bathroom is dirty,’ the unspoken goal is ‘clean the bathroom.’ Inferring both the desired outcome and the sequence of actions required to achieve it is second nature to humans but a significant challenge for AI. To address this, I introduce Intentionality—a novel framework that grounds human intentions in tasks by learning to infer the underlying goal and the sequence of steps required to accomplish it, given natural language instructions and visual context. This thesis advances multimodal grounded language learning by enabling AI to process multiple modalities and making it more capable of interpreting human intentions and interacting intuitively with its environment.