{"ID":23475217,"CreatedAt":"2026-09-18T01:09:05.407443952Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.20078","arxiv_id":"2609.20078","title":"FlipToSee: A Probabilistic Stable Placement Prior for Active Visual Exploration via Regrasping","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.","short_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 p...","url_abs":"https://arxiv.org/abs/2609.20078","url_pdf":"https://arxiv.org/pdf/2609.20078v1","authors":"[\"Chang Shu\",\"Sushil Samuel Dinesh\",\"Shinkyu Park\"]","published":"2026-09-17T11:36:28Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"LoRA\"]","has_code":false}