用专家混合模型实现复杂物体的通用抓握旋转，性能显著优于传统方法。
DexReMoE:In-hand Reorientation of General Object via Mixtures of Experts
- 采用多专家混合框架，针对不同复杂形状训练专用策略。
- 在150种新物体上平均连续成功率达19.5，最差情况提升至6.05。
- 适合需要高泛化能力的机器人灵巧操作任务。
手部物体旋转能力对灵巧操作至关重要，需具备鲁棒控制策略以应对多样物体几何形状、保持稳定抓握并执行精确复杂的朝向轨迹。然而，现有工作多聚焦单一或简单几何物体，难以推广至复杂形状。本文提出DexReMoE（灵巧抓握旋转专家混合模型），在混合专家（MoE）框架下训练多个针对不同复杂形状的专家策略，实现对广泛物体的泛化能力。同时引入物体类别信息作为额外输入，增强形状表征。该框架通过强化学习在仿真中训练，并在最具挑战性的场景——向下抓取手悬空旋转物体——中评估新分布外物体。平均连续成功次数达19.5，相比基线最差表现从0.69提升至6.05，验证了其在通用手部重定向任务中的可扩展性与适应性。
原文摘要 · Abstract (English)
In hand object reorientation provides capability for dexterous manipulation, requiring robust control policies to manage diverse object geometries, maintain stable grasps, and execute precise complex orientation trajectories. However, prior works focus on single objects or simple geometries and struggle to generalize to complex shapes. In this work, we introduce DexReMoE (Dexterous Reorientation Mixture-of-Experts), in which multiple expert policies are trained for different complex shapes and integrated within a Mixture-of-Experts (MoE) framework, making the approach capable of generalizing across a wide range of objects. Additionally, we incorporate object category information as privileged inputs to enhance shape representation. Our framework is trained in simulation using reinforcement learning (RL) and evaluated on novel out-of-distribution objects in the most challenging scenario of reorienting objects held in the air by a downward-facing hand. In terms of the average consecutive success count, DexReMoE achieves a score of 19.5 across a diverse set of 150 objects. In comparison to the baselines, it also enhances the worst-case performance, increasing it from 0.69 to 6.05. These results underscore the scalability and adaptability of the DexReMoE framework for general-purpose in-hand reorientation.
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