About the Research

This research develops computational methods that enable robots to perceive and understand their environment; move, see, orient and collaborate in complex missions with limited human intervention. As the focus is on robots that can be applied to the Army domain, of particular interest are highly efficient, robust, and agile methods that exhibit excellent properties with limited computational power, storage, and bandwidth.    

Opportunities exist in the following areas:

Robust perimeter defense systems
Robotic autonomy in mixed-initiative operations
Collaboration of heterogeneous robot teams in communications-limited environment
Autonomous navigation at operational tempo
Detection and tracking of moving objects from stationary and moving robots
Multi-robot object tracking, classification and recognition
GPS-denied localization of robots and objects in the scene
Reasoning over semantic concepts
Fusion of information from heterogeneous sensors for robot missions
Optimization of complex algorithms for computationally limited platforms
Experimentation and validation methods in robotics
Adaptive sampling of information in decision-making
Decision-making algorithms for human-robot collaborative tasks
Game theory applied for multi-agent learning 
Distributed optimization communication for collaborative multi-robot tasks

ARL Advisor: Carlos P. Nieto

ARL Advisor Email: carlos.p.nieto2.civ@army.mil

Explores foundational concepts and builds cumulative capabilities to simultaneously address multiple axes of complexity for future Robotics and Autonomous Systems (RAS) operational concepts.

The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at the CCDC Army Research Laboratory (ARL) help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs by pursuing scientific research and technological developments in diverse fields such as: applied mathematics, atmospheric characterization, simulation and human modeling, digital/optical signal processing, nanotechnology, material science and technology, multifunctional technology, combustion processes, propulsion and flight physics, communication and networking, and computational and information sciences.  

ARL’s Army Research Directorate (ARD) focuses on exploiting concept development, discovery, technology development, and transition of the most promising disruptive science and technology to deliver to the Army fundamentally advantageous science-based capabilities through laboratory’s 11 research competencies. This intramural research directorate also manages the laboratory’s essential research programs, which are flagship research efforts focused on delivering defined outcomes.

A complete application includes:

• Curriculum Vitae or Resume
• Three References Forms
  • An email with a link to the reference form will be available in Zintellect to the applicant upon completion of the on-line application. Please send this email to persons you have selected to complete a reference.
  • References should be from persons familiar with your educational and professional qualifications (include your thesis or dissertation advisor, if applicable)
• Transcripts
  • Transcript verifying receipt of degree must be submitted with the application. Student/unofficial copy is acceptable

If selected by an advisor the participant will also be required to write a research proposal to submit to the ARL-RAP review panel for :

• Research topic should relate to a specific opportunity at ARL (see Research Areas)
• The objective of the research topic should be clear and have a defined outcome
• Explain the direction you plan to pursue
• Include expected period for completing the study
• Include a brief background such as preparation and motivation for the research
• References of published efforts may be used to improve the proposal

A link to upload the proposal will be provided to the applicant once the advisor has made their selection.

Questions about this opportunity? Please email ARLFellowship@orau.org. 

• Degree: Doctoral Degree.
• Academic Level(s): Any academic level.
• Discipline(s):
  • Computer, Information, and Data Sciences (2 )
  • Engineering (6 )
  • Mathematics and Statistics (6 )
  • Physics (3 )