The paper compares Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) for cooperative motion planning of dual‑arm space manipulators tasked with reaching a target while avoiding inter‑arm collisions. A centralized state representation and multi‑objective reward function are used, and SAC consistently outperforms PPO across five random seeds, achieving an 80% success rate within 1.6 M steps versus PPO’s failure to reach this threshold within 4 M steps. Both algorithms degrade on unseen boundary targets, but SAC retains most of its performance while PPO collapses, revealing a reward‑loitering failure mode for PPO. whyItMatters":"The study demonstrates that off‑policy SAC is more robust and effective than on‑policy PPO for coordinating dual‑arm space robots, informing algorithm choice for active debris removal and on‑orbit servicing tasks."
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