Stud. assistant

Advertisement for the field of study such as: Automation engineering, electrical engineering, computer science, cybernetics, aerospace engineering, mechanical engineering, mathematics, mechatronics, physics, control engineering, software engineering, technical computer scienceor comparable. Focus is the setup of a simple robot cell in simulation and real hardware for robot grasping with an industrial robot using a robotic hand as an end effector. Core goals are generating robust grasp poses for a cube part with e.g. a learning-based algorithm, extending a heuristic-based search algorithm for efficient planning and grasping of parts, validating the algorithms in sim and real, and systematically evaluating limitations as complexity increases (e.g. more complex parts, bulk goods). The place of work would be in Heilbronn. What you will do Literature Review: Survey grasp pose generation (analytic vs. data-driven), grasp stability metrics, and grasp planning for robotic hands. Review sim-to-real for grasping, calibration methods, and control/trajectory planning in ROS 2 and Nvidia IsaacSim. Study strategies for clutter/bin-picking and bulk handling (perception, grasp sampling, anti-collision). Framework Development: Cell setup in sim and real: build a digital twin in Isaac Sim/Lab for the robot cell; mirror the real setup with matching kinematics, collision geometries, and dynamics. Algorithm Implementation: Implement and setup an algorithm to specify grasp poses for different parts with a specific robotic hand (position, orientation, approach vector, pre-grasp offset), contact constraints, force/torque limits, and success criteria; Extend and integrate the heuristic search algorithm for planning and grasping; Extend to more complex parts and more complex scenarios like bulk goods (clutter/bin). Simulation and Testing: create scripted test suites in Isaac Sim with domain randomization (poses, friction, lighting). Log success rate, cycle time, grasp stability, collision rate. Evaluate performance on varied shapes and bulk goods (single- vs. multi-object scenes), Identify boundary conditions (tolerances, reflectivity, small parts, heavy clutter), failure taxonomy, and mitigation proposals. What you bring to the table Enrolled student at a German university High motivation and initiative Very good English language skills Experience with Isaac Sim / Lab Experience in DRL is an advantage Independent, responsible, and structured working style What you can expect Pleasant working atmosphere Interesting and industry-relevant tasks dynamic, interdisciplinary team Insights into the future topic of automation Good technical equipment We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Remuneration according to the general works agreement for employing assistant staff. With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. Interested? Apply online now. We look forward to getting to know you Ms. Jennifer Leppich Recruiting +49 711 970-1415 jennifer.leppich@ipa.fraunhofer.de Fraunhofer Institute for Manufacturing Engineering and Automation IPA www.ipa.fraunhofer.de Requisition Number: 81686 Application Deadline: #J-18808-Ljbffr