双曲多视角预训练用于机器人操作
Hyperbolic Multiview Pretraining for Robotic Manipulation
AI总结：
本文提出HyperMVP，一种基于双曲空间的多视角自监督预训练框架，通过GeoLink编码器学习结构化嵌入，提升机器人操作任务的性能。
摘要：
3D感知的视觉预训练已被证明在提高下游机器人操作任务性能方面非常有效。然而，现有方法受限于欧几里得嵌入空间，其平坦的几何结构限制了它们对嵌入之间结构关系的建模能力。因此，它们难以学习出结构化的嵌入，这对于机器人应用中的稳健空间感知至关重要。为此，我们提出了HyperMVP，一种自监督框架，用于双曲多视角预训练。双曲空间提供了捕捉结构关系的几何特性。方法上，我们扩展了掩码自动编码器范式，并设计了一个GeoLink编码器来学习多视角双曲表示。预训练的编码器随后通过视觉-运动策略在操作任务上进行微调。此外，我们引入了3D-MOV，一个包含多种3D点云的大型数据集以支持预训练。我们在COLOSSEUM、RLBench和现实世界场景中评估了HyperMVP，结果在各种任务和扰动设置中均优于强基线。我们的结果突显了在非欧几里得空间中进行3D感知预训练在学习稳健且可推广的机器人操作策略方面的潜力。
英文摘要：
3D-aware visual pretraining has proven effective in improving the performance of downstream robotic manipulation tasks. However, existing methods are constrained to Euclidean embedding spaces, whose flat geometry limits their ability to model structural relations among embeddings. As a result, they struggle to learn structured embeddings that are essential for robust spatial perception in robotic applications. To this end, we propose HyperMVP, a self-supervised framework for \underline{Hyper}bolic \underline{M}ulti\underline{V}iew \underline{P}retraining. Hyperbolic space offers geometric properties well suited for capturing structural relations. Methodologically, we extend the masked autoencoder paradigm and design a GeoLink encoder to learn multiview hyperbolic representations. The pretrained encoder is then finetuned with visuomotor policies on manipulation tasks. In addition, we introduce 3D-MOV, a large-scale dataset comprising multiple types of 3D point clouds to support pretraining. We evaluate HyperMVP on COLOSSEUM, RLBench, and real-world scenarios, where it consistently outperforms strong baselines across diverse tasks and perturbation settings. Our results highlight the potential of 3D-aware pretraining in a non-Euclidean space for learning robust and generalizable robotic manipulation policies.
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。
