SimPLE: A visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects

Existing robotic systems have a clear tension between generality and precision. Deployed solutions for robotic manipulation tend to fall into the paradigm of one robot solving a single task, lacking precise generalization, i.e., the ability to solve many tasks without compromising on precision. This paper explores solutions for precise and general pick-and-place. In precise pick-and-place, i.e. kitting, the robot transforms an unstructured arrangement of objects into an organized arrangement, which can facilitate further manipulation. We propose simPLE (simulation to Pick Localize and PLacE) as a solution to precise pick-and-place. simPLE learns to pick, regrasp and place objects precisely, given only the object CAD model and no prior experience. We develop three main components: task-aware grasping, visuotactile perception, and regrasp planning. Task-aware grasping computes affordances of grasps that are stable, observable, and favorable to placing. The visuotactile perception model re..., , , # SimPLE: a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects

[https://doi.org/10.5061/dryad.vdncjsz3q](https://doi.org/10.5061/dryad.vdncjsz3q)

This dataset contains code, object CAD models, and experimental results for the paper \"SimPLE, a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects\".

## Description of the data and file structure

We provide a zip folder contain object CAD models as stl files, a zip folder containing the main repository for generating visuotactile perception models, and a pdf and excel sheet containing the experimental results for the precise placement trials.

The file simPLE_experimenal_results.xlsx contains details on the experimental outcome of each precise pick-and-place trial we ran. For each object, we provide the experimental outcome (in the column \"outcome\") for each of 20 trials of our method (in the column \"simPLE\"). For five of the objects, we additiona...

现有机器人系统在通用性与精度之间存在显著的固有矛盾。已部署的机器人操作解决方案往往陷入单机器人单任务的范式，缺乏精准泛化能力——即在不牺牲精度的前提下完成多种任务的能力。本文探索了实现精准且通用的拾取-放置任务的解决方案。在精准拾取-放置（即kitting（套件装配））中，机器人需将非结构化排布的物体转化为有序排布，这可为后续操作提供便利。本文提出simPLE（Simulation to Pick Localize and PLacE，拾取-定位-放置仿真方法）作为精准拾取-放置任务的解决方案。simPLE仅需物体的CAD模型即可学习精准的拾取、重新抓取与放置操作，无需任何先验经验。我们开发了三大核心组件：任务感知抓取、visuotactile perception（视觉触觉感知）以及重新抓取规划。任务感知抓取模块可计算稳定、可观测且利于后续放置的抓取可供性。视觉触觉感知模型re..., , , # SimPLE：一种基于仿真学习的精准拾取、定位、重新抓取与放置的视觉触觉方法

https://doi.org/10.5061/dryad.vdncjsz3q

本数据集包含对应论文《SimPLE：一种基于仿真学习的精准拾取、定位、重新抓取与放置的视觉触觉方法》的代码、物体CAD模型与实验结果。

## 数据与文件结构说明

我们提供了两个压缩文件夹：一个包含以STL格式存储的物体CAD模型，另一个包含用于生成视觉触觉感知模型的主代码仓库，此外还提供了一份记录精准放置试验结果的PDF文档与Excel表格。

文件simPLE_experimenal_results.xlsx包含了我们开展的所有精准拾取-放置试验的详细实验结果。针对每个物体，我们在"simPLE"列中记录了该方法针对该物体开展的20次试验的实验结果（结果记录于"outcome"列中）。对于其中5个物体，我们额外additiona...