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English(EN) PPO vs. SAC: A Comparative Study for Cooperative Motion Control of Dual-Arm Space Robots

软Actor-Critic在双臂空间机器人控制中优于Proximal Policy Optimization

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新研究论文比较了Proximal Policy Optimization (PPO) 和 Soft Actor-Critic (SAC) 在双臂空间机器人协同运动任务中的控制性能。研究发现，SAC在到达目标、保持姿态和避免碰撞方面显著优于PPO，在160万次环境交互步骤内达到了80%的成功阈值，而PPO在400万次交互步骤内未能达到该阈值。虽然两种算法在面对未见过目标时性能均有下降，但SAC仍保持了大部分性能，而PPO的性能则急剧下降，这凸显了SAC的鲁棒性以及PPO对奖励塑造的敏感性。 AI

影响 展示了Soft Actor-Critic在复杂机器人控制任务中优于Proximal Policy Optimization的性能，可能影响未来空间机器人领域的研究。

排序理由 比较两种强化学习算法在特定机器人应用中的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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软Actor-Critic在双臂空间机器人控制中优于Proximal Policy Optimization

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比较两种强化学习算法在特定机器人应用中的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nasim Fallahiarezoodar, Zheng H. Zhu ·

    PPO vs. SAC：双臂空间机器人协同运动控制的比较研究

    arXiv:2610.32034v1 Announce Type: new Abstract: The growing population of orbital debris has intensified interest in active debris removal and on-orbit servicing, for which dual-arm space manipulators offer improved grasping stability and load distribution relative to single-arm …

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