发表机构
Carnegie Mellon University; University of Texas at Arlington; General Motors（卡内基梅隆大学; 德克萨斯大学阿灵顿分校; 通用汽车公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人形足球中香蕉球技能进化失败问题，提出响应知情技能进化（RISE）方法，通过闭环目标延续和响应灵敏度排序，将普通踢球进化为高自旋曲线踢球，显著提升性能并实现硬件迁移。
AI 中文摘要
人形踢球需要协调的全身运动和精确的接触，而香蕉球踢法则要求产生球旋转和空气动力学弯曲的接触力学。运动模仿为普通踢球提供了可靠的先验，但强化学习可以在不改变底层踢球技术的情况下提高射门速度和落点精度。当任务目标在当前策略的响应上局部平坦时，即使奖励密集且优化保持稳定，将这种先验适应到本质上不同的接触丰富技能也可能失败。我们将这种条件称为一阶学习饥饿。为解决此问题，我们提出了响应知情技能进化（RISE），一种用于策略适应的闭环目标延续方法。RISE 使用从缓存回放中估计的响应灵敏度对受限目标变化进行排序，并且仅在接受更新时确保其产生已验证的响应进展，同时保持踢球可靠性。我们的分析表明，重新缩放饱和的自旋奖励无法在零自旋时恢复一阶灵敏度，而适应耦合的接触响应可以提供一条可学习的路径来产生自旋。我们将 RISE 集成到校准接触和马格努斯力空气动力学下的人形踢球流程中，并进行仿真到现实的迁移。实验表明，RISE 将普通踢球进化为高自旋曲线踢球，平均球自旋为 11.55 弧度/秒，相比学习进度课程将平均评估分数提高了 19.8%，并将关节目标达成率从 15.2% 提高到 50.9%。消融实验和响应诊断支持该机制，而 30 次动作捕捉记录的物理试验证明了学习到的曲线踢球在硬件上的一致迁移。项目网站：https://haozhang-thu.github.io/bananakick/
英文摘要
Humanoid kicking requires coordinated whole-body motion and precise contact, while a banana kick demands contact mechanics that generate ball spin and aerodynamic curvature. Motion imitation provides a reliable ordinary-kick prior, but reinforcement learning may improve shot speed and placement accuracy without changing the underlying kicking technique. Adapting this prior to a qualitatively different contact-rich skill can fail even when the reward is dense and optimization remains stable. The failure occurs when the task objective is locally flat over the current policy's responses. We term this condition first-order learning starvation. To address it, we propose response-informed skill evolution (RISE), a closed-loop objective-continuation method for policy adaptation. RISE ranks bounded objective changes using response sensitivity estimated from cached rollouts and accepts updates only when they produce verified response progress while preserving kicking reliability. Our analysis shows that rescaling a saturated spin reward cannot recover first-order sensitivity at zero spin, whereas adapting coupled contact responses can provide a learnable path to spin generation. We integrate RISE into a humanoid kicking pipeline under calibrated contact and Magnus-force aerodynamics, and sim-to-real transfer. Experiments show that RISE evolves the ordinary kick into a high-spin curved kick with 11.55 rad/s mean ball spin, improves the mean evaluation score by 19.8% over a learning-progress curriculum, and raises joint target attainment from 15.2% to 50.9%. Ablations and response diagnostics support the mechanism, while 30 motion-capture-recorded physical trials demonstrate consistent hardware transfer of the learned curved kick. Project website: https://haozhang-thu.github.io/bananakick/
