This position is part of the National Institute of Standards (NIST) Professional Research Experience
(PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at
academic institutions on specific projects of mutual interest, thus requires that such institutions must be
the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to
work on scientific research in many areas. Employees in this position will perform technical work that
underpins the scientific research of the collaboration.
Research Title:
Robotic Grasping and Manipulation Researcher
The work will entail:
NIST is investigating the performance of commercial and custom AI systems (hardware and software) for
advanced robotic grasping and manipulation systems, with a focus on grasp path planning and
graspability analysis for improved autonomy. The work will involve implementing tactile sensing and
developing control strategies for dextrous, multi-finger hands, alongside research into bi-manual
manipulation techniques, to conduct experiments that evaluate the efficiency and adaptability of
robotic systems in complex environments.
Key responsibilities will include but are not limited to:
-Evaluate and benchmark commercial and custom AI systems (hardware and software) to
advance autonomous robotic grasping and manipulation capabilities.
-Research and develop algorithms for grasp path planning and graspability analysis to improve
decision-making and autonomy in unstructured environments.
-Integrate tactile sensors into robotic fingertips/end-effectors and develop signal processing,
data analysis, and force-control strategies to achieve finger force sensitivity.
-Design and implement control strategies for high-degree-of-freedom, dexterous multi-finger
hands and coordinate bi-manual manipulation techniques for dual-arm systems.
-Conduct rig-based and simulation-based experiments to test, evaluate, and benchmark system
efficiency, adaptability, and performance in complex manufacturing or assembly environments.
-Write technical reports, contribute to peer-reviewed publications, and deliver weekly
presentations to showcase project milestones and research progress.
-Work Schedule: On-campus (Gaithersburg, MD), Full-Time (40 hrs / week)
Qualifications:
Candidates must be eligible to obtain a Department of Commerce background check for facility access.
Education:
Engineering / Computer Science majors with Master’s Degree or Ph.D, or in the final
year of degree (e.g., Computer Science, Robotics, Mechanical Engineering or similar)
-Strong technical background in robotic manipulation, kinematics, grasp path planning, and bi-
manual control strategies.
-Practical experience with multi-finger, high-dexterity robotic hands and end-effectors.
Knowledge of tactile sensing principles, sensor integration, signal processing, and force-
feedback control.
-Experience with computer vision and sensor fusion for 2D/3D grasp pose estimation and
graspability analysis.
-Strong programming proficiency in Python and C++.
-Hands-on experience with ROS / ROS 2 and motion planning toolkits (e.g., MoveIt).
-Familiarity with AI/ML frameworks (e.g., PyTorch, TensorFlow) for learning-based grasping and
force sensing strategies.
-Experience with robotics simulation platforms and physics engines (e.g., NVIDIA IsaacSim,
Gazebo, MuJoCo, Drake).
-Experience with version control tools (Git, GitHub, GitLab, Bitbucket).
-Experience working on Linux/Unix operating systems.
-Working knowledge of CAD software (e.g., SolidWorks, OnShape) for test fixture or end-effector
integration.
The university is an Equal Employment Opportunity employer that does not unlawfully discriminate in any of its programs or activities on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity or expression, or on any other basis prohibited by applicable law.