We present an active embodiment identification method for legged robots that jointly learns information-seeking behavior and explicit embodiment prediction. Using a history-augmented URMA architecture, the method infers joint-level and global embodiment parameters through interaction with the environment in simulation across different morphologies.

翻译：我们提出了一种面向足式机器人的主动本体辨识方法，该方法能够联合学习信息寻求行为与显式本体预测。通过采用历史增强型URMA架构，本方法可在不同形态的仿真环境中，通过与环境交互推断关节级与全局级本体参数。