arXiv 2407.151612025-09-04cs.ROcs.AIcs.LG

FFHFlow：通过流变分推理生成多样化且具备不确定性感知的灵巧抓取

FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference

School of Information Computation and Technology, Technical University of Munich (TUM)（技术大学慕尼黑信息计算技术学院）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电研究所）；Department of Informatics, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院信息学院）

Qian Feng, Jianxiang Feng, Zhaopeng Chen, Rudolph Triebel, Alois Knoll

备注：: First two authors contributed equally, whose ordering decided via coin-tossing. Accepted for CoRL 2025

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AI总结：

针对局部观测下多指手抓取生成的多样性与不确定性建模难题，提出基于归一化流的变分框架FFHFlow，通过分层流形学习与不确定性感知排序，在抓取多样性、成功率及采样效率上优于现有方法，适用于杂乱受限场景。

摘要：

从局部观测中为多指手合成具备多样性、不确定性感知能力的抓取姿态，仍是机器人学习领域的一项关键挑战。现有的生成式方法难以对灵巧手复杂的抓取分布进行建模，且通常无法推理局部点云固有的形状不确定性，导致抓取结果不可靠或过于保守。我们提出FFHFlow，这是一种基于流的变分框架，可生成多样且鲁棒的多指抓取结果，同时能显式量化局部点云中的感知不确定性。我们的方法利用基于归一化流的深度潜变量模型学习分层抓取流形，克服了条件变分自编码器（cVAEs）存在的模式崩溃和先验刚性的局限。通过利用流的可逆性和精确似然性，FFHFlow能够内省局部观测中的形状不确定性，识别新的物体结构，从而实现具备风险感知的抓取合成。为进一步提升可靠性，我们将判别式抓取评估器与流似然相结合，提出了一种具备不确定性感知的排序策略，优先选择对形状模糊性鲁棒的抓取姿态。在仿真和真实场景下开展的大量实验表明，FFHFlow在抓取多样性和成功率上均优于包括扩散模型在内的当前最优基线方法，同时还能实现运行时高效采样。我们还展示了其在杂乱和受限环境中的实用价值，在这类场景中，多样性驱动的采样可通过减少碰撞展现出优越性能（项目页面：https://sites.google.com/view/ffhflow/home/）。

英文摘要：

Synthesizing diverse, uncertainty-aware grasps for multi-fingered hands from partial observations remains a critical challenge in robot learning. Prior generative methods struggle to model the intricate grasp distribution of dexterous hands and often fail to reason about shape uncertainty inherent in partial point clouds, leading to unreliable or overly conservative grasps. We propose FFHFlow, a flow-based variational framework that generates diverse, robust multi-finger grasps while explicitly quantifying perceptual uncertainty in the partial point clouds. Our approach leverages a normalizing flow-based deep latent variable model to learn a hierarchical grasp manifold, overcoming the mode collapse and rigid prior limitations of conditional Variational Autoencoders (cVAEs). By exploiting the invertibility and exact likelihoods of flows, FFHFlow introspects shape uncertainty in partial observations and identifies novel object structures, enabling risk-aware grasp synthesis. To further enhance reliability, we integrate a discriminative grasp evaluator with the flow likelihoods, formulating an uncertainty-aware ranking strategy that prioritizes grasps robust to shape ambiguity. Extensive experiments in simulation and real-world setups demonstrate that FFHFlow outperforms state-of-the-art baselines (including diffusion models) in grasp diversity and success rate, while achieving run-time efficient sampling. We also showcase its practical value in cluttered and confined environments, where diversity-driven sampling excels by mitigating collisions (Project Page: https://sites.google.com/view/ffhflow/home/).

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。