| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | January 30, 2023 |
| Latest Amendment Date: | May 29, 2024 |
| Award Number: | 2239301 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Jie Yang
jyang@nsf.gov (703)292-4768 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | May 1, 2023 |
| End Date: | April 30, 2028 (Estimated) |
| Total Intended Award Amount: | $600,000.00 |
| Total Awarded Amount to Date: | $246,505.00 |
| Funds Obligated to Date: |
FY 2024 = $126,732.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3451 WALNUT ST STE 440A PHILADELPHIA PA US 19104-6205 (215)898-7293 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3330 Walnut Street PHILADELPHIA PA US 19104-6228 |
| 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): | Robust Intelligence |
| Primary Program Source: |
01002425DB NSF RESEARCH & RELATED ACTIVIT 01002526DB NSF RESEARCH & RELATED ACTIVIT 01002627DB NSF RESEARCH & RELATED ACTIVIT 01002728DB NSF RESEARCH & RELATED ACTIVIT |
| 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
Humans excel at prioritizing and attending to different parts of the environment as the need arises. For example, a nurse feeding a patient might carefully consider individual morsels of food on a plate while picking them up, but afterwards devote his/her attention to the patient's mouth and facial expressions. By comparison, today's robots are rigid and inflexible in the ways that they observe, process, and interact with the world. They either focus on everything at the cost of becoming sluggish or instead cut corners indiscriminately and become prone to failures. This project will build innovative software technologies to allow general-purpose robots to flexibly adapt to task requirements, much like humans can, improving their agility and efficiency and making it easier for them to learn new tasks. This will enable applications of such robots not only in the household and hospital tasks which this project will use to develop the research, but also in many other socially relevant settings as farms, constructions sites, and small-scale manufacturing. Two graduate students and several undergraduate students will receive research training directly through this project. Further, this project will also draw from its research findings to improve graduate and undergraduate courses and summer outreach courses for high-school students.
This project explores the hypothesis that one key missing piece is agile and efficient perception-action loops that can evolve on-the-fly in response to task requirements. Consider a robot feeding a child food from a rice bowl. The locations and shapes of individual morsels of food, and the child's detailed pose and mouth configuration are all relevant over the course of the task and in each of the eating phases. Such time-varying task requirements are ubiquitous, yet they are poorly accounted for by the locked-in abstractions in today's standard computer vision algorithms and control loops. This project aims to advance visual recognition approaches in the context of embodied action, and to develop robot learning approaches in tandem that exploit these advances. It redesigns control loops to be flexible according to task demands, hierarchically factorized to permit novel compositions of the factor components, and self-learnable for scalability. To this end, it develops a staged but end-to-end differentiable control loop structure with interleaved task-specific, fine-tunable components and task-generic, reusable component stages. It further proposes novel active self-learning approaches that exploit the agent's own embodiment to teach it. These advances will enable improved sample efficiency for robot learning approaches as well as computational efficiency for task execution.
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.
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