标题： 基于自进化先验的极端单足运动跳跃启动强化学习 显示英文标题

标题： Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion

摘要： 强化学习（RL）在使四足机器人实现敏捷运动方面显示出巨大潜力。 然而，由于早期阶段的不稳定性交互和不可靠的奖励反馈，直接训练策略同时应对极端欠驱动和极端地形等双重极端挑战，如单足跳跃任务中所遇到的，仍然极具挑战性。 为了解决这个问题，我们提出了JumpER（通过自我演化先验的跳跃启动强化学习），这是一种将策略学习分为多个复杂度逐步增加阶段的RL训练框架。 通过迭代自举之前学到的策略动态生成自我演化先验，JumpER逐步优化并增强指导，从而在不依赖外部专家先验或手工设计奖励塑造的情况下稳定探索和策略优化。 具体而言，当与一个结构化的三阶段课程相结合，该课程逐步演化动作模态、观测空间和任务目标时，JumpER使四足机器人首次能够在不可预测的地形上实现稳健的单足跳跃。 值得注意的是，所产生的策略有效处理了传统方法难以克服的挑战性场景，包括最大达60厘米的宽缝、不规则间距的楼梯以及距离在15厘米到35厘米之间的踏石。 因此，JumpER为应对极端欠驱动和极端地形的双重挑战提供了系统且可扩展的解决方案。 显示英文摘要

摘要： Reinforcement learning (RL) has shown great potential in enabling quadruped robots to perform agile locomotion. However, directly training policies to simultaneously handle dual extreme challenges, i.e., extreme underactuation and extreme terrains, as in monopedal hopping tasks, remains highly challenging due to unstable early-stage interactions and unreliable reward feedback. To address this, we propose JumpER (jump-start reinforcement learning via self-evolving priors), an RL training framework that structures policy learning into multiple stages of increasing complexity. By dynamically generating self-evolving priors through iterative bootstrapping of previously learned policies, JumpER progressively refines and enhances guidance, thereby stabilizing exploration and policy optimization without relying on external expert priors or handcrafted reward shaping. Specifically, when integrated with a structured three-stage curriculum that incrementally evolves action modality, observation space, and task objective, JumpER enables quadruped robots to achieve robust monopedal hopping on unpredictable terrains for the first time. Remarkably, the resulting policy effectively handles challenging scenarios that traditional methods struggle to conquer, including wide gaps up to 60 cm, irregularly spaced stairs, and stepping stones with distances varying from 15 cm to 35 cm. JumpER thus provides a principled and scalable approach for addressing locomotion tasks under the dual challenges of extreme underactuation and extreme terrains.