フローマッチングと粒子フィルタの混合による固有感覚ベースのオンライン接触推定
Mixture of Flow Matching and Particle Filter for Proprioception-Based Online Contact Estimation
関節トルクとベース力覚のみから接触数・位置・力を推定するため、粒子フィルタにフローマッチング生成モデルを組み込み、多峰性や接触イベントを扱う手法を提案した。実機で単一接触0.36cm・二重接触0.88cmの精度を達成。
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著者: Seo Wook Han, Min Jun Kim
分類: cs.RO
原文アブストラクト
In this article, we propose ContactFlowFilter, a novel approach for contact estimation that determines the number of active contacts, their locations, and the associated force vectors. We consider a robot manipulator equipped with joint torque sensors and a base force/torque sensor; such a proprioceptive-only setup presents a challenging inverse problem characterized by (i) multi-modality from non-unique solutions, (ii) temporal dependencies in state evolution, and (iii) discrete contact events involving contact addition/removal. Existing approaches, such as particle filters (PFs) and learning-based methods, often struggle to address these challenges simultaneously. To overcome this, ContactFlowFilter integrates a generative model into the PF framework. The recursive structure of the PF captures temporal dependencies, while the flow matching model serves as an auxiliary proposal distribution to track multi-modal beliefs and detect contact events. Extensive validation in both simulation and real-world experiments confirms the effectiveness of the proposed method. In real-world settings, ContactFlowFilter achieves the lowest dissimilarity from the target distribution, with errors of only 0.36 cm and 0.88 cm for single- and dual-contact scenarios, respectively, while operating at 0.69 ms and 2.69 ms.