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About

Lennart Wachowiak is a rising researcher at the intersection of human-robot interaction (HRI), explainable AI (XAI), and large language models (LLMs). His work focuses on when and how robots should explain themselves to build trust, and whether LLMs can align with human social intuitions for collaborative robotics. His most-cited paper, "When Do People Want an Explanation from a Robot?" (19 citations), tackles the critical timing of explanations in HRI—a key to successful human-agent collaboration. He also explores the embodiment of LLMs in "Exploring Spatial Schema Intuitions in Large Language and Vision Models" (5 citations), probing whether these models grasp physical intuition. Wachowiak has developed a taxonomy of explanation types and need indicators, and a time-series pipeline to detect interaction ruptures from user reactions (4 citations). His work on aligning LLMs with people’s social intuitions for HRI (2 citations) further demonstrates his commitment to making AI socially aware. With a growing citation impact and contributions to both theoretical frameworks and practical detection tools, Wachowiak is shaping how robots become more transparent, responsive, and trustworthy partners.