用物理引导的扩散模型生成更稳定的物体摆放方案。
Generating Stable Placements via Physics-guided Diffusion Models
- 将稳定性融入扩散模型采样过程，结合几何先验与物理约束
- 在四个基准场景中提升56%抗扰动能力，运行时间减少47%
- 无需微调，可直接用于现成模型，适合机器人抓取场景
在多物体场景中稳定放置物体是机器人操作的基础挑战，要求无穿透、精确接触面和力平衡。现有方法依赖仿真或基于外观的启发式评估。本文将稳定性直接整合到扩散模型的采样过程中：通过离线采样规划器获取多模态放置标签，训练扩散模型以点云为条件生成几何感知的稳定放置。利用基于分数的生成模型的组合特性，将学习到的先验与稳定性感知损失结合，提高从高稳定性区域采样的概率。该策略无需额外训练或微调，可直接应用于现成模型。在四个可精确计算稳定性的基准场景上评估，物理引导模型在面对强力扰动时稳定性提升56%，同时运行时间降低47%。
原文摘要 · Abstract (English)
Stably placing an object in a multi-object scene is a fundamental challenge in robotic manipulation, as placements must be penetration-free, establish precise surface contact, and result in a force equilibrium. To assess stability, existing methods rely on running a simulation engine or resort to heuristic, appearance-based assessments. In contrast, our approach integrates stability directly into the sampling process of a diffusion model. To this end, we query an offline sampling-based planner to gather multi-modal placement labels and train a diffusion model to generate stable placements. The diffusion model is conditioned on scene and object point clouds, and serves as a geometry-aware prior. We leverage the compositional nature of score-based generative models to combine this learned prior with a stability-aware loss, thereby increasing the likelihood of sampling from regions of high stability. Importantly, this strategy requires no additional re-training or fine-tuning, and can be directly applied to off-the-shelf models. We evaluate our method on four benchmark scenes where stability can be accurately computed. Our physics-guided models achieve placements that are 56% more robust to forceful perturbations while reducing runtime by 47% compared to a state-of-the-art geometric method.
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