Collaborative Research: NRI: INT: Scalable, Customizable, Robot Learning with Humans
合作研究:NRI:INT:可扩展、可定制、与人类一起学习的机器人
基本信息
- 批准号:2024675
- 负责人:
- 金额:$ 70万
- 依托单位:
- 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 2023-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Activities of daily living (ADLs) are both essential and routine aspects of self-care, including the ability to independently eat, dress, transfer from one position to another, bathe, and toilet. Robotic assistance with activities of daily living could increase the independence of people with disabilities, improve quality of life, and help address pressing societal needs, such as aging populations, high healthcare costs, and shortages of healthcare workers. While progress has been made towards such robotic-assistance, a key challenge is that many activities of daily living require robots to manipulate fabric in coordination with people. Notably, many forms of bedside assistance include dexterous manipulation of bedding, hygiene often involves dexterous manipulation of washcloths and towels, and dressing involves a diverse array of clothes. This project seeks to make foundational progress on this major challenge through advancements in machine learning, simulation, and customizable human-robot interaction. This project will result in new capabilities in robot-assisted bedding adjustment, bathing, and dressing for people with disabilities. In addition, this project and its participating research groups will broaden participation by engaging under-represented groups, K-12 students, and undergraduates in research and education.This research aims to develop a co-robotic framework towards the Integrative Task: Customizable Fabric Manipulation for Home Assistance, with specific emphasis on bedding, bathing, and dressing, which have significant physical interaction between the human and the robot. This project will make foundational progress along three main thrusts, brought together under this overarching integrative application: (1) Learning to Assist from Raw Sensory Observations, (2) Physics Simulation for Learning Assistance, and (3) Customizable Robotic Assistance with Human-in-the-Loop Feedback. Customizability here refers to the ability of our system to adapt to the specific preferences and abilities of each individual. Through open-sourced code development and physics simulations, this project also seeks to lower the barrier to entry for others to work towards the general problem of customizable home assistance. Overall, this research will develop new techniques in robot-assisted dressing, bedding adjustment, and body bathing (hygiene) for people with disabilities, which has the potential to help millions of people achieve greater independence and a higher quality of life.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.
日常生活活动(ADL)是自我照顾的基本和常规方面,包括独立吃饭、穿衣、从一个位置转移到另一个位置、洗澡和厕所的能力。具有日常生活活动的机器人辅助可以增加残疾人的独立性,提高生活质量,并有助于解决紧迫的社会需求,如人口老龄化、医疗保健成本高和医护人员短缺。虽然在这种机器人辅助方面已经取得了进展,但一个关键的挑战是,许多日常生活活动都需要机器人与People协作来操纵织物。值得注意的是,许多形式的床边协助包括灵活地处理床上用品,卫生经常涉及灵活地处理毛巾和毛巾,而穿衣涉及各种各样的衣服。该项目寻求通过在机器学习、模拟和可定制的人-机器人交互方面的进步,在这一重大挑战上取得基础性进展。该项目将为残疾人带来机器人辅助调整床上用品、洗澡和穿衣的新能力。此外,该项目及其参与的研究小组将通过让代表不足的群体、K-12学生和本科生参与研究和教育来扩大参与。本研究旨在开发一个面向综合任务的合作机器人框架:家庭援助的可定制织物操作,特别强调床上用品、洗澡和穿衣,这些都是人和机器人之间重要的物理交互。该项目将沿着三个主要方面取得基础性进展,在这个总体综合应用程序下:(1)从原始感官观察中学习辅助,(2)用于学习辅助的物理模拟,以及(3)具有人在回路反馈的可定制机器人辅助。在这里,可定制化是指我们的系统适应每个人的特定偏好和能力的能力。通过开源代码开发和物理模拟,该项目还寻求降低进入门槛,让其他人致力于解决可定制家庭援助的一般问题。总体而言,这项研究将为残疾人开发机器人辅助穿衣、床上用品调整和沐浴(卫生)方面的新技术,这有可能帮助数百万人实现更大的独立性和更高的生活质量。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(16)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Learning Representations that Enable Generalization in Assistive Tasks
能够泛化辅助任务的学习表示
- DOI:10.48550/arxiv.2212.03175
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Jerry Zhi;Aditi Raghunathan;Daniel S. Brown;Zackory M. Erickson;A. Dragan
- 通讯作者:A. Dragan
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
- DOI:
- 发表时间:2021-03
- 期刊:
- 影响因子:0
- 作者:Lili Chen;Kimin Lee;A. Srinivas;P. Abbeel
- 通讯作者:Lili Chen;Kimin Lee;A. Srinivas;P. Abbeel
Masked World Models for Visual Control
- DOI:10.48550/arxiv.2206.14244
- 发表时间:2022-06
- 期刊:
- 影响因子:0
- 作者:Younggyo Seo;Danijar Hafner;Hao Liu;Fangchen Liu;Stephen James;Kimin Lee;P. Abbeel
- 通讯作者:Younggyo Seo;Danijar Hafner;Hao Liu;Fangchen Liu;Stephen James;Kimin Lee;P. Abbeel
Masked Trajectory Models for Prediction, Representation, and Control
- DOI:10.48550/arxiv.2305.02968
- 发表时间:2023-05
- 期刊:
- 影响因子:0
- 作者:Philipp Wu;Arjun Majumdar;Kevin Stone;Yixin Lin;Igor Mordatch;P. Abbeel;A. Rajeswaran
- 通讯作者:Philipp Wu;Arjun Majumdar;Kevin Stone;Yixin Lin;Igor Mordatch;P. Abbeel;A. Rajeswaran
Masked Autoencoding for Scalable and Generalizable Decision Making
- DOI:10.48550/arxiv.2211.12740
- 发表时间:2022-11
- 期刊:
- 影响因子:0
- 作者:Fangchen Liu;Hao Liu;Aditya Grover;P. Abbeel
- 通讯作者:Fangchen Liu;Hao Liu;Aditya Grover;P. Abbeel
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Pieter Abbeel其他文献
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning
论微调与元强化学习的有效性
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Mandi Zhao;Pieter Abbeel;Stephen James - 通讯作者:
Stephen James
Data fitting with geometric-programming-compatible softmax functions
- DOI:
10.1007/s11081-016-9332-3 - 发表时间:
2016-08-04 - 期刊:
- 影响因子:1.700
- 作者:
Warren Hoburg;Philippe Kirschen;Pieter Abbeel - 通讯作者:
Pieter Abbeel
Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs
使用对象可组合 NeRF 缩小视觉模拟与真实的差距
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Nikhil Mishra;Maximilian Sieb;Pieter Abbeel;Xi Chen - 通讯作者:
Xi Chen
Any-point Trajectory Modeling for Policy Learning
用于政策学习的任意点轨迹建模
- DOI:
10.48550/arxiv.2401.00025 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Chuan Wen;Xingyu Lin;John So;Kai Chen;Qi Dou;Yang Gao;Pieter Abbeel - 通讯作者:
Pieter Abbeel
Tokenized and continuous embedding compressions of protein sequence and structure
蛋白质序列和结构的标记化与连续嵌入压缩
- DOI:
10.1016/j.patter.2025.101289 - 发表时间:
2025-06-13 - 期刊:
- 影响因子:7.400
- 作者:
Amy X. Lu;Wilson Yan;Kevin K. Yang;Vladimir Gligorijevic;Kyunghyun Cho;Pieter Abbeel;Richard Bonneau;Nathan C. Frey - 通讯作者:
Nathan C. Frey
Pieter Abbeel的其他文献
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{{ truncateString('Pieter Abbeel', 18)}}的其他基金
Doctoral Student Career Development at the Workshop on the Algorithmic Foundations of Robotics (WAFR)
机器人算法基础研讨会(WAFR)上的博士生职业发展
- 批准号:
1648643 - 财政年份:2016
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
CAREER: Apprenticeship Learning for Robotic Manipulation of Deformable Objects
职业:可变形物体的机器人操作学徒学习
- 批准号:
1351028 - 财政年份:2014
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
NRI-Large: Collaborative Research: Multilateral Manipulation by Human-Robot Collaborative Systems
NRI-Large:协作研究:人机协作系统的多边操纵
- 批准号:
1227536 - 财政年份:2012
- 资助金额:
$ 70万 - 项目类别:
Continuing Grant
RI: Small: Large-Scale Machine Learning for Connectomics
RI:小型:连接组学的大规模机器学习
- 批准号:
1118055 - 财政年份:2011
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
CPS: Medium: Learning for Control of Synthetic and Cyborg Insects in Uncertain Dynamic Environments
CPS:中:学习在不确定的动态环境中控制合成昆虫和机器人昆虫
- 批准号:
0931463 - 财政年份:2009
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
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- 批准号:10774081
- 批准年份:2007
- 资助金额:45.0 万元
- 项目类别:面上项目
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