RI: Small: Taming Combinatorial Challenges in Multi-Object Manipulation

RI：小：克服多对象操纵中的组合挑战

基本信息

批准号：
1617744
负责人：
Kostas Bekris
金额：
$ 46.84万
依托单位：
Rutgers University New Brunswick
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617744&HistoricalAwards=false
关键词：
RI Small Taming Combinatorial Challenges

项目摘要

A key challenge in many autonomous robot manipulation applications isthe rearrangement of multiple objects. There are two situations wheresuch needs arise: (i) the manipulation task itself is to rearrangeobjects, and (ii) occluding items must be rearranged to allow therobot access to the target object(s). Examples of such scenarios canarise in warehouses and industrial setups, where a robot has tofrequently select, pick and transfer products, packages and pallets inthe presence of many other similar objects. Another example comes fromservice robotics, where a robotic assistant that operates in a humanspace has to frequently retrieve or rearrange multiple items placed innarrow spaces, such as objects in shelves.This project investigates which classes of multi-object manipulationplanning can be efficiently addressed given progress in multi-bodymotion planning and develops a powerful suite of novel computationalsolutions. The key insight is that for many real-world rearrangementtasks the sequence of object motions to solve the problem, ignoringgrasping aspects, look similar to solutions of multi-body motionplanning, especially for similar sized objects. The study of this linkreveals it is possible to cast certain multi-object manipulationproblems as a "pebble motion problem on a graph", which is wellstudied in algorithmic theory and multi-body motion planning. Theoverall objective is to provide rigorous methods with desirablecompleteness and optimality guarantees for multi-object manipulation,which exhibit good scalability and efficiency for problems wherecurrent methods face issues with the inherent combinatorialcomplexity. Such methods could also be used as guiding heuristics fortasks with additional constraints, such as non-trivial dynamics anduncertainty.

许多自主机器人操纵应用中的一个关键挑战是多个物体的重新排列。有两种情况会出现这种需求：（i）操作任务本身就是重新排列对象，以及（ii）必须重新排列遮挡物品以允许机器人访问目标对象。此类场景的例子常见于仓库和工业设施中，机器人必须在存在许多其他类似物体的情况下频繁地选择、拾取和转移产品、包装和托盘。另一个例子来自服务机器人，在人类空间中操作的机器人助手必须经常检索或重新排列放置在狭窄空间中的多个物品，例如架子上的物品。该项目研究了在给定进展的情况下，可以有效解决哪些类别的多对象操纵规划。多体运动规划并开发了一套强大的新颖计算解决方案。关键的见解是，对于许多现实世界的重新排列任务，解决问题的对象运动序列（忽略抓取方面）看起来类似于多体运动规划的解决方案，特别是对于相似大小的对象。该链接的研究表明，可以将某些多对象操纵问题转化为“图上的卵石运动问题”，这在算法理论和多体运动规划中得到了充分研究。总体目标是为多对象操作提供具有理想完整性和最优性保证的严格方法，对于当前方法面临固有组合复杂性问题的问题表现出良好的可扩展性和效率。这些方法还可以用作具有额外约束的任务的指导启发法，例如非平凡的动态和不确定性。