让机器人双手协作更灵活，能根据姿势自动调整动作。
ManiDP: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation
- 用黎曼概率模型编码双手姿态特征，指导动作生成
- 实测成功率提升39.33%，任务匹配度提高0.45
- 适合需要精细双臂协同的机器人操作场景
近期研究展示了扩散模型在机器人双臂技能学习中的潜力。然而，现有方法忽视了姿势依赖性任务特征的学习，而这类特征对灵巧双臂操作中满足特定力和速度需求至关重要。为此，我们提出了一种新的模仿学习方法——可操纵性感知扩散策略（ManiDP），不仅能生成合理的双臂轨迹，还能优化双臂构型以更好满足姿势依赖的任务要求。ManiDP通过从专家示范中提取双臂可操纵性，并使用基于黎曼的概率模型编码其中蕴含的姿势特征，再将这些特征融入条件扩散过程，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双臂任务上评估了ManiDP，实验结果表明，其平均操作成功率相比基线方法提升了39.33%，任务兼容性提高了0.45。本工作强调了将姿势相关机器人先验知识融入双臂技能扩散的重要性，以实现类人般的适应性和灵巧性。
原文摘要 · Abstract (English)
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.
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