NSF award 2550106 · Clemson University

Collaborative Research: III: Small: Closing Sim-to-Real Gap in Reinforcement Learning via Randomization, Alignment, and Derivation

Final report due

29 December 2027

in 437 days · The award ends on 31 August 2027.

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Decide about an extension3 May 2027in 197 days
award ending 2027-08-31 · Research.gov
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Extension request17 Jul 2027in 272 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2027in 307 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2027in 437 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2027in 437 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2027in 437 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 31 August 2027.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2027.

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Award card

Award number: 2550106
Period of support: 1 Sep 2025 – 31 Aug 2027
Amount: $290,739
Awardee: Clemson University, Clemson, SC
Program: Info Integration & Informatics
Directorate: CSE

Abstract

Reinforcement Learning (RL) is a machine learning paradigm that strives to make optimal decision-making based on experience acting in an environment. In many cases, the "environment" refers to a simulator in the training stage and refers to the real world in the deployment stage. Training in the simulator brings a lot of advantages: lower cost, more safety, and more flexibility. However, it is almost impossible to…

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