外观
论文arXiv 2610.1268420 天前
通过基于扩散的轨迹规划实现移动机器人非抓取多物体放置
原文链接：Non-Prehensile Multi-Object Placement with Mobile Robots via Diffusion-Based Trajectory Planning（arXiv 2610.12684 · 发布日期：2026-10-08）
Non-Prehensile Multi-Object Placement with Mobile Robots via Diffusion-Based Trajectory Planning
针对移动机器人多物体精确非抓取放置，提出因果分解扩散规划，并在Push-T、多箱环境和TurtleBot 3上验证。
核心要点
- 移动机器人纠正精确放置误差常需绕物体大范围重定位。
- 将多物体推动形式化为紧容差下的多目标分配问题。
- 因果分解扩散规划可为单个机器人计划采样多个物体结果。
- Push-T成功率最高且不合理运动最低；多箱环境提升21.5个百分点。
验证范围与局限：验证范围包括Push-T、三个类仓库多箱推动环境和一台TurtleBot 3 Burger真机。
英文摘要 (Abstract)
Non-prehensile manipulation with mobile robots has primarily targeted placement in coarse goal regions. Precision placement has been studied with manipulators on benchmarks such as Push-T, where an omnidirectional end effector can readily re-approach an object from any side. For a mobile robot, correcting the same placement errors may require wide repositioning maneuvers around the object. We formalize multi-object pushing with a mobile robot as a multi-target assignment problem at tight tolerances. We propose a diffusion planner with a causal factorization: each robot denoising step is followed by an object step conditioned on the current clean robot estimate. This factorization allows us to sample multiple object outcomes for a single robot plan, capturing the multi-modality of pushing. We also design a set of pushing-specific costs for mobile robots, used for both guidance and best-of-$N$ sampling at inference. On Push-T, our planner attains the highest success rate of the methods we compare, while having the lowest rate of implausible object motion. Across three multi-box pushing environments resembling warehouse scenarios, it raises the fraction of boxes placed by $21.5$ percentage points over an unguided baseline, and replans $1.8\times$ faster than a planner that denoises the two trajectories together. Lastly, we deploy the guided planner on a TurtleBot 3 Burger with object shapes absent from the training data, where it places $85.0%$ of objects within tolerance against Diffusion Policy's $37.5%$.
作者：Lance Pharand, Megnath Ramesh, Mark Chiu, Yash Vardhan Pant, Stephen L. Smith
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