PREP0005092 Robotic Grasping Researcher

Robotic Grasping and Manipulation Researcher

Project Description:

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: 

• 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

Desired Qualifications: 

• US Citizen Preferred
• Master’s Degree or Ph.D in Engineering or Computer Science 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

Other Details:

• Full-time: the participant is expected to work 40 hours a week
• Location: the participant will work at the NIST Gaithersburg Campus.
• Duration: this is expected to be a one-year position. Extensions are sometimes granted depending on the availability of funds.