Adam Jelley
Papers
1
Total Citations
4
H-Index
1
About
Adam Jelley is a researcher at the forefront of embodied AI and human-robot interaction, with a primary focus on aligning large language models (LLMs) with individual human preferences for real-world robotics. His key research areas include task planning for service robots, reinforcement learning from human feedback, and personalized autonomous systems. Jelley’s major contribution, exemplified in his highly cited work "LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots" (2024, 4 citations), introduces a novel framework that bridges the gap between generic LLM planners and the nuanced, context-specific needs of household environments. By leveraging reinforced self-training, his approach enables robots to adapt their behavior—such as tidying or cooking—based on a user’s unique routines and preferences, moving beyond one-size-fits-all solutions. This work has quickly garnered attention for its practical impact, laying the groundwork for more intuitive and cooperative home assistants. Jelley’s research not only advances the technical frontier of LLM-guided robotics but also addresses a critical challenge in human-centered AI, making him a notable emerging voice in the field.
Research Focus
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Top Papers
- 1