To understand and collaborate with humans, robots must account for individual human traits, habits, and activities over time. However, most robotic assistants lack these abilities, as they primarily focus on predefined tasks in structured environments and lack a human model to learn from. This work introduces COOPERA, a novel framework for COntinual, OPen-Ended human-Robot Assistance, where simulated humans, driven by psychological traits and long-term intentions, interact with robots in complex environments. By integrating continuous human feedback, our framework, for the first time, enables the study of long-term, open-ended human-robot collaboration (HRC) in different collaborative tasks across various time-scales. Within COOPERA, we introduce a benchmark and an approach to personalize the robot's collaborative actions by learning human traits and context-dependent intents. Experiments validate the extent to which our simulated humans reflect realistic human behaviors and demonstrate the value of inferring and personalizing to human intents for open-ended and long-term HRC. Project Page: https://dannymcy.github.io/coopera/

That's why we've created the AI Hype Index--a simple, at-a-glance summary of everything you need to know about the state of the industry. More than 70 countries went to the polls in 2024. The good news is that this year of global elections turned out to be largely free from any major deepfake campaigns or AI manipulation. Instead we saw lots of AI slop: buff Trump, Elon as ultra-Chad, California as catastrophic wasteland. While some worry that development of large language models is slowing down, you wouldn't know it from the steady drumbeat of new products, features, and services rolling out from itty-bitty startups and massive incumbents alike.