ManiDP：姿态相关的双臂操作的可操作性感知扩散策略
ManiDP: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation
AI总结：
针对现有双臂操作学习忽略姿态相关特征的问题，提出可操作性感知扩散策略ManiDP，通过提取双臂可操作性并编码姿态特征引导扩散生成，在六个真实任务上成功率提升39.33%。
摘要：
近期工作展示了扩散模型在机器人双臂技能学习中的潜力。然而，现有方法忽略了姿态相关任务特征的学习，而这些特征对于调整双臂配置以满足灵巧双臂操作中特定的力和速度要求至关重要。为解决这一局限，我们提出了可操作性感知扩散策略（ManiDP），一种新颖的模仿学习方法，它不仅生成合理的双臂轨迹，还优化双臂配置以更好地满足姿态相关的任务要求。ManiDP通过从专家演示中提取双臂可操作性，并使用基于黎曼的概率模型对封装后的姿态特征进行编码来实现这一点。这些编码后的姿态特征随后被纳入条件扩散过程，以引导生成与任务兼容的双臂运动序列。我们在六个真实世界的双臂任务上评估了ManiDP，实验结果表明，与基线方法相比，平均操作成功率提高了39.33%，任务兼容性提高了0.45。这项工作强调了将姿态相关的机器人先验知识整合到双臂技能扩散中，以实现类人适应性和灵巧性的重要性。
英文摘要：
Recent work has demonstrated the potential of diffusion models in robot bimanual skill learning. However, existing methods ignore the learning of posture-dependent task features, which are crucial for adapting dual-arm configurations to meet specific force and velocity requirements in dexterous bimanual manipulation. To address this limitation, we propose Manipulability-Aware Diffusion Policy (ManiDP), a novel imitation learning method that not only generates plausible bimanual trajectories, but also optimizes dual-arm configurations to better satisfy posture-dependent task requirements. ManiDP achieves this by extracting bimanual manipulability from expert demonstrations and encoding the encapsulated posture features using Riemannian-based probabilistic models. These encoded posture features are then incorporated into a conditional diffusion process to guide the generation of task-compatible bimanual motion sequences. We evaluate ManiDP on six real-world bimanual tasks, where the experimental results demonstrate a 39.33$\%$ increase in average manipulation success rate and a 0.45 improvement in task compatibility compared to baseline methods. This work highlights the importance of integrating posture-relevant robotic priors into bimanual skill diffusion to enable human-like adaptability and dexterity.
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。
