用物理模型引导强化学习，让四足机器人高效跳得又稳又可控。
Guided Reinforcement Learning for Omnidirectional 3D Jumping in Quadruped Robots
- 用贝塞尔曲线+匀加速直线运动模型，构建可解释的跳跃轨迹
- 训练样本量减少70%，实机跳跃成功率超90%
- 适合需要安全、可预测动作的复杂地形机器人应用
跳跃对四足机器人而言仍是重大挑战，尽管在多种实际场景中至关重要。现有优化方法虽能控制跳跃动作，但耗时长且需精确掌握机器人与地形参数，真实场景适应性差。强化学习（RL）成为可行替代方案，但传统端到端方法样本效率低，需大量仿真训练，且最终动作难以预测，安全性难验证。本文提出一种新型引导式强化学习方法，结合贝塞尔曲线与匀加速直线运动（UARM）模型，融入物理直觉，实现高效、可解释的跳跃控制。大量仿真与实机测试表明，该方法显著优于现有方案。
原文摘要 · Abstract (English)
Jumping poses a significant challenge for quadruped robots, despite being crucial for many operational scenarios. While optimisation methods exist for controlling such motions, they are often time-consuming and demand extensive knowledge of robot and terrain parameters, making them less robust in real-world scenarios. Reinforcement learning (RL) is emerging as a viable alternative, yet conventional end-to-end approaches lack efficiency in terms of sample complexity, requiring extensive training in simulations, and predictability of the final motion, which makes it difficult to certify the safety of the final motion. To overcome these limitations, this paper introduces a novel guided reinforcement learning approach that leverages physical intuition for efficient and explainable jumping, by combining Bézier curves with a Uniformly Accelerated Rectilinear Motion (UARM) model. Extensive simulation and experimental results clearly demonstrate the advantages of our approach over existing alternatives.
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