Tweet Announces HARBOR for Autonomous Robot Policy Training

Zechu Li posted on X about HARBOR, described as agentic RL automation for robot learning. The approach uses a single prompt and simulator to build tasks, design rewards, train policies, tune parameters, and evaluate results. A video attachment displays the user interface with a dark background and a terminal running the prompt. The announcement positions the system as producing a trained robot policy from prompt to policy in an autonomous manner.