外观
论文arXiv 2607.240363 个月前
WARL：用于腿式机器人任务无关学习的力旋量增强强化学习
原文链接：WARL: Wrench-Augmented Reinforcement Learning for Task-Agnostic Learning in Legged Robots（arXiv 2607.24036 · 发布日期：2026-07-27）
WARL: Wrench-Augmented Reinforcement Learning for Task-Agnostic Learning in Legged Robots
针对腿式机器人关节空间探索受限，WARL将力旋量引入动作空间，并在四足机器人多地形与运动任务上验证。
核心要点
- 腿式机器人强化学习虽有较高运动性能，但关节空间动作限制了探索能力。
- WARL在动作空间加入力与力矩，结合力旋量引导探索和基于成功率的课程机制。
- 四足机器人实验显示，该方法无需地形特定奖励调整或复杂课程即可鲁棒学习。
- 消融验证Switching Curriculum有效；但力旋量可能诱发未充分利用机体的行为。
验证范围与局限：验证范围为四足机器人上的多样地形和运动任务。
英文摘要 (Abstract)
While reinforcement learning for legged robots has achieved high motor performance, it has been constrained by the limited exploration capability of actions confined to the joint space. To address this issue, this study proposes a new method, Wrench-Augmented Reinforcement Learning (WARL), which introduces a wrenche (force and torque) into the action space. The proposed method combines wrench-guided exploration with a success rate-based curriculum mechanism to expand exploration capabilities in the early stages of learning, with the ultimate goal of acquiring behaviors based solely on joint control. Experiments using a quadruped robot demonstrated that WARL can learn robustly across diverse terrains and motor tasks without requiring terrain-specific reward adjustments or complex curriculum designs. Furthermore, an ablation study verified the effectiveness of the Switching Curriculum, which gradually eliminates the wrench. On the other hand, we also show that introducing a wrench can encourage behaviors that do not sufficiently exploit the robot's physical embodiment. These findings suggest that while wrench-based exploration enhancement is effective for improving learning efficiency, designing it in a way that is consistent with the robot's physical structure is a critical future challenge.
作者：K. Yoneda, Kento Kawaharazuka, Kei Okada
来源
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