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Research Spending & Results

Award Detail

Awardee:RUTGERS, THE STATE UNIVERSITY OF NEW JERSEY
Doing Business As Name:Rutgers University New Brunswick
PD/PI:
  • Abdeslam Boularias
  • (848) 932-0150
  • abdeslam.boularias@rutgers.edu
Co-PD(s)/co-PI(s):
  • Jingjin Yu
  • Mridul Aanjaneya
Award Date:09/13/2021
Estimated Total Award Amount: $ 1,490,276
Funds Obligated to Date: $ 1,490,276
  • FY 2021=$1,490,276
Start Date:02/01/2022
End Date:01/31/2026
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:NRI: Robust and Efficient Physics-based Learning and Reasoning in Degraded Environments
Federal Award ID Number:2132972
DUNS ID:001912864
Parent DUNS ID:001912864
Program:NRI-National Robotics Initiati
Program Officer:
  • Erion Plaku
  • (703) 292-8695
  • eplaku@nsf.gov

Awardee Location

Street:33 Knightsbridge Road
City:Piscataway
State:NJ
ZIP:08854-3925
County:Piscataway
Country:US
Awardee Cong. District:06

Primary Place of Performance

Organization Name:Rutgers University New Brunswick
Street:33 Knightsbridge Road
City:Piscataway
State:NJ
ZIP:08854-8019
County:Piscataway
Country:US
Cong. District:06

Abstract at Time of Award

This project will perform fundamental research into developing and integrating physics-driven reasoning and planning techniques to enable autonomous robots to manipulate unknown irregular objects and navigate in unstructured, dynamic environments. The developed techniques will be deployed on RoboMantis: a four-legged, wheeled robot that can assist in first-response missions. The project will fill the important gap between existing research on learning models of unknown objects from data and research on developing adequate simulation tools for robotic manipulation and locomotion by answering three fundamental questions: 1) How to efficiently simulate the effects of robotic actions on objects with uncertain models? 2) How to use physics simulation tools to plan manipulation and locomotion strategies for navigating in unstructured terrains? and, 3) How to learn physical models of objects on the fly? The project builds on top of progress in computer vision, physics simulation, and planning, towards developing an efficient toolset for robotic navigation in rubble. The main technical objectives of this project are to: 1) Develop physics simulation tools that can be used for efficiently inferring models of both rigid and non-rigid objects and for robust planning, 2) Develop optimization tools for learning models of objects from limited vision and interaction data, 3) Develop manipulation and navigation algorithms that can leverage the learned models, and 4) Demonstrate the fully integrated system on a diverse range of tasks related to search and rescue operations, such as manipulating unknown objects in clutter and navigating in rubble. The project will adopt a Bayesian approach where models of objects that are typically found in piles of rubble, such as debris and rocks, will be inferred from a few RGB-D images providing partial views of the scenes, and also from their responses to forces applied by the robot during locomotion and manipulation actions. Hypotheses of various models will be used to simulate the effects of the exerted forces on the objects. Models that best reproduce the observed effects of the forces will be given the highest probabilities. The inferred models will then be used to plan robust manipulation and locomotion actions that allow the robot to clear its way and advance through a pile of debris. The project brings together an interdisciplinary team of investigators who have expertise in computer vision, physics simulation and planning. Implementations of the developed solutions will be provided to the research community as open-source software packages. This will be coupled with the generation of educational material, especially programming assignments on manipulation challenges that require physics reasoning, which will be shared with the academic community. The material will aim to attract undergraduate students early in their studies to STEM by using hands-on experience that can be provided with the use of robotics, while also exposing them to foundational methods and data-driven tools. When appropriate, efforts will be made to introduce the research, in particular the hardware demonstrations, to K-12 students to cultivate their early interests in robotics, which touches all aspects of STEM. In the process, the PIs will aim to leverage diversity programs at Rutgers University to recruit and support underrepresented groups. 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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