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

arXiv:2609.20078 · cs.RO · Submitted 2026-09-17 · Read on arXiv

cs.RO

Submitted: 2026-09-17

Updated: 2026-09-17

Terminology

• Robustness — 55× in this paper · explained in 5 episodes
• Point Clouds — 10× in this paper · explained in 2 episodes
• Verification — 6× in this paper · explained in 2 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Information Gain — 3× in this paper · explained in 2 episodes
• Mixture Density Network — 3× in this paper
• Alignment — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• King Abdullah University of Science and Technology — 1× in this paper
• Paper Review — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes

Sources

• Generating Stable Placements via Physics-guided Diffusion Models
• cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving