FlipToSee: A Probabilistic Stable Placement Prior for Active Visual Exploration via Regrasping

2026-09-17

AI Summary

为了解决主动视觉探索中物体重新定向问题，提出FlipToSee框架，通过单视点云学习概率性放置先验，并使用von Mises-Fisher混合密度网络建模。

Abstract

Active visual exploration of tabletop objects often requires reorienting an unknown resting object onto a different stable support face to expose occluded surfaces. To identify such placements without exhaustive physical search, we learn a probabilistic placement prior from a single-view point cloud. Stable placement prediction is inherently multimodal, and conventional 6-DoF regression introduces further ambiguity by modeling translation and in-plane yaw. We therefore propose FlipToSee, a probabilistic framework that removes this representational ambiguity by parameterizing placements as unit support normals on $S^2$ while modeling their multimodal conditional distribution via a von Mises--Fisher mixture density network. To decouple mode diversity from physical robustness, FlipToSee deterministically extracts a compact candidate set from the mixture components and applies robustness-aware reranking using an auxiliary head trained with candidate-aligned supervision. In simulation, FlipToSee achieves $98.4\%$ first-proposal success on in-distribution objects, $95.3\%$ on out-of-distribution shapes, and $90.0\%$ under zero-shot transfer to household YCB objects. We further demonstrate the learned placement prior on a physical robot by integrating it with grasp and motion planning for exploratory regrasping.

Problem

• Active Visual Exploration
• Stable Placement
• Point Cloud

Innovation

• probabilistic framework
• unit support normals
• von Mises--Fisher mixture density network
• robustness-aware reranking