用扩散模型从触觉数据推断物体姿态，解决接触不确定性问题。
Diffusion-based Inverse Observation Model for Artificial Skin
- 用扩散模型学习触觉数据到物体姿态的逆映射
- 在模拟中高效生成符合接触约束的姿态假设
- 适合做触觉感知与机器人抓取的研究者
基于接触的物体姿态估计因观测不连续且存在多解性而困难，同一触觉观测可能对应多个系统状态。这种多模态特性使得在满足接触约束条件下高效采样有效假设变得复杂。扩散模型可通过去噪算法学习从此类多模态概率分布中生成样本。本文利用其概率建模能力，学习一个以分布式人工皮肤采集的触觉测量为条件的逆观测模型。通过模拟实验展示了通过触觉实现物体姿态估计时，高效采样接触假设的能力。
原文摘要 · Abstract (English)
Contact-based estimation of object pose is challenging due to discontinuities and ambiguous observations that can correspond to multiple possible system states. This multimodality makes it difficult to efficiently sample valid hypotheses while respecting contact constraints. Diffusion models can learn to generate samples from such multimodal probability distributions through denoising algorithms. We leverage these probabilistic modeling capabilities to learn an inverse observation model conditioned on tactile measurements acquired from a distributed artificial skin. We present simulated experiments demonstrating efficient sampling of contact hypotheses for object pose estimation through touch.
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