| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 26, 2019 |
| Latest Amendment Date: | August 26, 2019 |
| Award Number: | 1939930 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Erion Plaku
eplaku@nsf.gov (703)292-0000 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2019 |
| End Date: | August 31, 2021 (Estimated) |
| Total Intended Award Amount: | $49,952.00 |
| Total Awarded Amount to Date: | $49,952.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
807 UNION ST SCHENECTADY NY US 12308-3256 (518)388-6101 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
807 Union Street Schenectady NY US 12308-3103 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | NRI-National Robotics Initiati |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Soft robots are a compelling new platform for operating alongside humans in unstructured, rugged, and dynamic environments. However, as of yet, very few soft robots are field-deployable in scenarios such as search-and-rescue and disaster response. This is due in part to the challenge of finding ways of making soft robots move effectively. The central aim of this project is to establish methods by which soft robots can autonomously develop environment-specific task repertoires with little or no prior knowledge about their own abilities or the surrounding environment. These new techniques will allow robots to quickly and efficiently retrain themselves when they are damaged or when their task environment changes. Importantly, this work will also establish a model for involving and developing undergraduate students as independent researchers in the high risk, high payoff field of soft robotics, thereby growing the community of researchers and lowering the barriers of entry for the next generation of robotics researchers.
Specifically, this project will use of Quality Diversity Algorithms to efficiently and autonomously discover effective soft robotic behaviors that allow them to robustly and adaptively move in complex environments. These techniques will be developed using low-cost dynamically complex tensegrity-based robots. The specific goals of this research are to produce insights into how soft robots can autonomously explore the range of their abilities, producing multimodal repertoires of behaviors that fully leverage their dynamics, and to develop methods by which these robots can robustly and efficiently adapt their repertoires in response to damage and unexpected environmental change. Throughout, this effort will involve substantial hardware-based validation and testing using a high speed, high resolution motion capture system.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The nascent field of soft robotics seeks to build completely flexible adaptive robots that compete with natural systems in terms of robustness and adaptability. Soft robots have important applications ranging from search and rescue to biomedicine to planetary exploration. The ability to develop robots that can quickly, autonomously, and flexibly respond to the consequences of natural disasters is a key national interest. Unfortunately, the very properties which make soft robots so appealing also make them difficult to accurately model, scalably design, and robustly control. As a consequence, very few soft robots are yet field-deployable in high demand co-robotic scenarios such as search-and-rescue and disaster response.
This grant provided funding to support undergraduate research at Union College centered around tackling one of the grand challenges of soft robotics: how to control soft materials in ways that exploit their complex dynamics. Specifically, the central aim of this undergraduate-driven research was to establish methods by which soft robots can autonomously develop environment-specific behavioral repertoires with little or no prior knowledge about their ownabilities or the surrounding environment.
This research contributed to our understanding ofhow soft systems, both natural and artificial, effectively and efficiently learn their full range of capabilities, allowing them to adapt to damage and changing environments. More broadly, this work shed light on the complex interplay between bodies and brains in both natural and artificial systems, lending insights into how soft- bodied animals such as caterpillars and octopuses are able to operate in complex environments.
Additionally, this funding provided for the research training and mentorship of a large cohort of undergraduates at Union College - including students from historically under-represented groups - several of whom have already moved on to prestigious graduate programs and successful careers in industry.
Last Modified: 01/29/2022
Modified by: John A Rieffel
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