Robotic Grasping and Manipulation Researcher

Overview

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.

Qualifications

• 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.

Research Proposal

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)