Firehose

This paper proposes a runtime system called REA that enables the coordination of fast, reactive policies (like those used in vision-language models) with slower, deliberative brain models, allowing them to work together effectively. Practitioners might care about REA because it can improve the performance and efficiency of embodied systems like robots.

This paper develops a system called RoboArchitect that automates the design of robotic agents by learning to optimize their harnesses, which determine when the agent interacts with the environment. A practitioner might care about this because it could significantly improve the performance of robotic agents without requiring significant updates to their underlying models.

This paper develops a new framework called HiFOR that helps vision-language-action models predict and plan for future events in a more accurate way, which can improve their performance in tasks like robotic manipulation. Practitioners can care about this research because it could lead to more reliable and effective robots that can adapt to changing situations.

This paper develops a new method to evaluate proactive agents, which are systems that decide when and how to assist without being asked. Practitioners in AI and robotics might care because it helps them understand how well these systems work and make them more effective.

This paper develops a robot policy that allows for real-time control over quality attributes like speed and smoothness, and shows that this approach can improve both task success and quality in real-world deployment scenarios.

This paper introduces WebAnything, a framework that automatically generates reusable website environments from real URLs, allowing for reproducible training and evaluation of web agents. Practitioners might care about this because it simplifies the creation of diverse and realistic environments for training AI models.