2025

Indoor fungal contamination poses significant risks to public health, yet existing detection methods are slow, costly, and lack spatial resolution. Conventional approaches rely on laboratory analysis or high-concentration sampling, making them unsuitable for real-time monitoring and scalable deployment. We introduce \textbf{\textit{Scensory}}, a robot-enabled olfactory system that simultaneously identifies fungal species and localizes their spatial origin…

We present Sym2Real, a fully data-driven framework that provides a principled way to train low-level adaptive controllers in a highly data-efficient manner. Using only about 10 trajectories, we achieve robust control of both a quadrotor and a racecar in the real world, without expert knowledge or simulation tuning. Our approach achieves this data efficiency by…

Training reinforcement learning agents with human feedback is crucial when task objectives are difficult to specify through dense reward functions. While prior methods rely on offline trajectory comparisons to elicit human preferences, such data is unavailable in online learning scenarios where agents must adapt on the fly. Recent approaches address this by collecting real-time scalar…

This workshop focuses on ethics education with emergent technologies that emphasize the future role of engineers to responsibly innovate, develop, and deploy for various shareholders. Our team of Duke faculty from biomedical engineering, mechanical engineering, and electrical & computer engineering have successfully integrated ethics education modules in biotechnology, robotics, automation, and machine learning within technical…