学习四足机器人的感知平台自适应运动控制器
Learning Perceptive Platform Adaptive Locomotion Controllers for Quadrupedal Robots
AI总结：
研究如何将感知融入形态感知强化学习架构，通过自适应地形课程训练跨形态通用控制器，发现仅评论家感知在鲁棒性和稳定性上优于完全感知策略。
摘要：
通用四足运动仍然受到跨不同机器人形态整合感知的困难限制。最先进的控制器依赖于单机器人训练或忽略实时感知的盲目策略，导致跨本体泛化能力差。设计在相关四足形态间保持鲁棒且融入感知的运动策略具有挑战性。此外，完全感知策略通常对噪声敏感，而盲目控制器缺乏地形感知。在这项工作中，我们研究如何将感知融入形态感知强化学习架构以实现可部署的四足控制。基于MorAL，我们使用自适应地形课程在多个参考四足机器人上训练形态专用通用控制器。我们比较了盲目基线、评论家感知变体（MorAL+）和完全感知演员-评论家（PPAL）。在平坦和崎岖地形上进行仿真评估，并在ANYmal硬件上部署。结果表明，仅评论家感知在鲁棒性和跟踪一致性上优于盲目基线，同时在感知噪声下比完全感知策略更稳定。这些发现表明感知放置和课程设计是可扩展、形态感知运动的关键因素。
英文摘要：
Universal quadrupedal locomotion remains limited by the difficulty of integrating perception across diverse robot morphologies. State-of-the-art controllers rely on single-robot training or blind policies that omit real-time perception, leading to poor cross-embodiment generalization. Designing locomotion policies that remain robust across related quadruped morphologies while incorporating perception is challenging. Moreover, fully perceptive policies are often sensitive to noise, whereas blind controllers lack terrain awareness. In this work, we study how perception should be integrated into morphology-aware reinforcement learning architectures for deployable quadrupedal control. Building on MorAL, we train morphology-specialized universal controllers on multiple reference quadrupeds using adaptive terrain curricula. We compare a blind baseline, a critic-perceptive variant (MorAL+), and a fully perceptive actor-critic (PPAL). Policies are evaluated in simulation on flat and rough terrains, and deployed on ANYmal hardware. Results show that critic-only perception improves robustness and tracking consistency over blind baselines while remaining more stable than fully perceptive policies under perception noise. These findings highlight that perception placement and curriculum design are key factors for scalable, morphology-aware locomotion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
