NRI: INT: Collaborative Research: Buoyancy-assisted Collaborative Robots That are Cheap, Safe, and Never Fall Down.

NRI：INT：协作研究：廉价、安全且永不摔倒的浮力辅助协作机器人。

基本信息

批准号：
2024949
负责人：
Stefanos Nikolaidis
金额：
$ 45万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024949&HistoricalAwards=false
关键词：
NRI INT Collaborative Research Buoyancy

项目摘要

This National Robotics Initiative project will promote the progress of science, advance the national prosperity, welfare, and security; by developing novel buoyancy-assisted collaborative robots that are cheap, safe, and never fall down. The current state of robotics systems is not ready to be deployable to human daily environments, which are challenging for a robot to navigate, especially for legged robots. While they are naturally well-suited for human environments by design, legged robots are often too expensive to scale up for a multi-agent setup, heavy and dangerous to operate near humans, and hard to guarantee stable locomotion. To this end, the ultimate goal of this research is to build collaborative robots that are cheap, safe, and never fall down by exploiting buoyancy to defy a significant amount of gravity This research has the potential for significant impact in enabling deployed locomotion for safe robotic systems interactively assisting humans in daily environments. It provides a principled way for leveraging large amounts of safe and scalable hardware designs with recent advances in machine learning techniques to develop compact representations that are transferable across different robotic systems and human environments. This will innovate how one can utilize a deployable multi-agent system in disaster relief zones and large outdoor environments. The project team will invite general public participation by publicizing the hardware designs and open-sourcing all the deployed software infrastructure. The grant will also support a competition for middle and high school students using the developed low-cost platforms with the goal to foster students’ interest in science, technology, engineering and math (STEM). Creating a new class of locomotion systems has two major challenges: designing a new hardware that is cheap and safe and developing an algorithm for locomotion and collaboration. In order to address these two challenges, this grant will support development of a novel framework that (1) addresses a fundamentally new family of legged robots, namely buoyancy assisted robots (BARs), which are constantly stood-up and highly light-weight by leveraging the inherent "lifting power" of buoyancy; and (2) empowers BARs with reliable locomotion and collaboration skills using deep reinforcement learning algorithms. To this end, the research team will perform interrelated research thrusts centered around the goal of safe, scalable multi-robot systems: developing multiple BARs with each unique locomotion style, learning primitive motor skills for a single agent, extending individually trained agents to orchestrate in a large-scale multi-agent friendly environment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

《国家机器人倡议》将促进科学的进步，推进国家繁荣，并进行安全的浮力 - 浮标协作机器人，这些机器人便宜，安全并且永远不会掉落。人类的日常环境对于机器人来说是挑战的，它可以通过设计来驾驶适合人类环境的LL，腿部机器人通常会扩大规模，以进行多机构设置，重重和危险，可在人类附近进行操作，并难以保证稳定的运动。为此，这是建筑物的最终目标。赚取紧凑型代表的技术，这将使Thiloss不同的系统和人类环境都可以创新。软件基础结构还将支持中学和高中生的竞争，以培养学生对科学，技术，工程和数学的兴趣（STEM）。廉价，安全且可以安全地使用算法的新硬件，以应对这两个挑战，该赠款将支持开发一个新颖的框架，该框架（1）解决了一个基本的腿部机器人家族，即浮力辅助机器人（酒吧），通过利用浮力固有的起重力量“浮力”；（2）具有可靠的运动和协作技巧的皇帝使用深厚的强化学习算法。为此，研究团队将执行Interlelelarch，推力将SABE的目标居中。智力优点和更广泛的影响审查标准。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

pyribs: A Bare-Bones Python Library for Quality Diversity Optimization

DOI：
10.1145/3583131.3590374
发表时间：
2023-03
期刊：
Proceedings of the Genetic and Evolutionary Computation Conference
影响因子：
0
作者：
Bryon Tjanaka;Matthew C. Fontaine;David H. Lee;Yulun Zhang;Nivedit Reddy Balam;N. Dennler;Sujay S. Garlanka;Nikitas Dimitri Klapsis;S. Nikolaidis
通讯作者：
Bryon Tjanaka;Matthew C. Fontaine;David H. Lee;Yulun Zhang;Nivedit Reddy Balam;N. Dennler;Sujay S. Garlanka;Nikitas Dimitri Klapsis;S. Nikolaidis

Demonstration-Guided Reinforcement Learning with Learned Skills