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About

Jessica Landon is a leading researcher in embodied AI and human-robot interaction, whose work focuses on building artificial agents that can perceive, act, and communicate naturally alongside humans. Her most influential paper, “Imitating Interactive Intelligence” (2020, 43 citations), lays out a foundational vision for designing robots that sense the world, assist with physical tasks, and converse through natural language—a goal long confined to science fiction. Building on this, her 2021 study “Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning” (32 citations) demonstrates how agents can learn complex, real-world behaviours by combining imitation learning with self-supervision, enabling more fluid and adaptable human-robot collaboration. Most recently, in “Vision-Language Models as Success Detectors” (2023, 11 citations), Landon pioneers the use of large pretrained vision-language models to automatically detect successful task completion—a critical step toward generalisable reward systems for training intelligent agents. Her work is shaping the next generation of interactive AI, bridging the gap between simulated environments and physical deployment. With over 86 citations across her top papers, Landon is a rising voice in the quest for robots that truly understand and assist us.