English(EN) PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots

新的强化学习方法允许四足机器人在运行时调整运动偏好

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了PROMO，一种用于四足机器人的新型偏好条件多目标强化学习方法。该方法允许单一运动策略在运行时适应不同的操作员偏好，平衡诸如命令跟踪、稳定性和能效等目标。PROMO在模拟中展示了广泛的帕累托覆盖率和可预测的偏好响应，在转移到Unitree Go2机器人后，在能效、位置误差和身体姿态偏差方面取得了显著改进。 AI

影响通过将操作员意图与固定的奖励函数分离，实现了更具适应性和用户可控性的机器人运动。

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Amr Mousa, Rifny Rachman, Neil Karavis, Michele Caprio, Richard Allmendinger · 2026-10-02 04:00

PROMO：用于四足机器人的偏好条件多目标强化学习

arXiv:2610.01260v1 Announce Type: cross Abstract: Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforcement learning (RL) hardcodes these priorities into a fixed scalar reward at tra…

本文如何被排名

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PROMO：用于四足机器人的偏好条件多目标强化学习

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