Web Science and Digital Libraries Research Group

Figure 2 Hafner et al. : The authors of this work consider four visual domains, including robot locomotion and manipulation tasks, Atari games with 2D graphics, DMLab, and Minecraft. DreamerV3 is successful in all of these diverse domains, demonstrating its ability to handle spatial and temporal reasoning challenges.  In my last post , Paper Summary: "Beyond Classifiers: Remote Sensing Change Detection with Metric Learning" Zhang et al., I reviewed methods to detect discrete changes in temporal visual data. But what if we're concerned with the fidelity of simulated or generative data vs. the real world? In my work at NASA, I study machine learning methods for training autonomous systems in simulation. One of the biggest problems with this research direction is the Simulation-to-Reality problem, where training in simulation can result in relatively high uncertainty due to differences between the simulated representation of the environm...