一政策通用：面向跨躯体操作的几何感知动作潜在表示
One-Policy-Fits-All: Geometry-Aware Action Latents for Cross-Embodiment Manipulation
AI总结：
本文提出One-Policy-Fits-All框架，通过几何感知潜在表示和统一潜在重定向解码器，实现跨躯体操作的统一策略学习，提升数据效率和技能迁移效果。
摘要：
跨躯体操作对于提高机器人操作的可扩展性和降低数据收集成本至关重要。然而，不同躯体之间的显著差异，如动作空间的变化和结构差异，给多数据源的联合训练带来了挑战。为此，我们提出One-Policy-Fits-All (OPFA)，一个能够跨多个躯体学习单一、通用策略的框架。我们首先学习几何感知潜在表示（GaLR），利用3D卷积网络和变换器构建不同躯体之间的共享潜在动作空间。然后设计了一个统一的潜在重定向解码器，从潜在表示中提取特定躯体的动作，而无需任何特定躯体解码器的微调。OPFA实现了来自不同躯体的数据端到端联合训练，包括各种夹具和灵活手，具有任意自由度，显著提高了数据效率并降低了技能迁移成本。我们在11种不同的末端执行器上进行了广泛实验。结果表明，OPFA通过利用异构躯体数据显著提高了在不同环境中的策略性能。例如，跨躯体联合训练相比单源训练可将成功率提高超过50%。此外，通过添加仅几个新躯体的演示（如8个），OPFA可以实现与72个演示训练的模型相当的性能。
英文摘要：
Cross-embodiment manipulation is crucial for enhancing the scalability of robot manipulation and reducing the high cost of data collection. However, the significant differences between embodiments, such as variations in action spaces and structural disparities, pose challenges for joint training across multiple sources of data. To address this, we propose One-Policy-Fits-All (OPFA), a framework that enables learning a single, versatile policy across multiple embodiments. We first learn a Geometry-Aware Latent Representation (GaLR), which leverages 3D convolution networks and transformers to build a shared latent action space across different embodiments. Then we design a unified latent retargeting decoder that extracts embodiment-specific actions from the latent representations, without any embodiment-specific decoder tuning. OPFA enables end-to-end co-training of data from diverse embodiments, including various grippers and dexterous hands with arbitrary degrees of freedom, significantly improving data efficiency and reducing the cost of skill transfer. We conduct extensive experiments across 11 different end-effectors. The results demonstrate that OPFA significantly improves policy performance in diverse settings by leveraging heterogeneous embodiment data. For instance, cross-embodiment co-training can improve success rates by more than 50% compared to single-source training. Moreover, by adding only a few demonstrations from a new embodiment (e.g., eight), OPFA can achieve performance comparable to that of a well-trained model with 72 demonstrations.
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。
