Action on the Fly

"Action on the fly" research focuses on developing systems capable of adapting and making decisions in real-time, without pre-planning or extensive offline training. Current efforts concentrate on improving efficiency in various domains, including energy-efficient deep learning accelerators, Monte Carlo Tree Search algorithms with efficient action abstraction, and robust robot control through on-the-fly adaptation of learned behaviors and real-time human feedback. This research is significant for advancing artificial intelligence in resource-constrained environments and enabling more adaptable and responsive robots and intelligent systems in dynamic real-world scenarios.