Robotics Software Developer Postbaccalaureate Appointee - Rapid Prototyping Laboratory

The Rapid Prototyping Laboratory (RPL) in the Data Science and Learning division builds self-driving laboratories, where robots, scientific instruments and AI work together to run experiments with minimal human intervention. We are seeking a Postbachelor Appointee to develop the robotics software that makes these laboratories work, and to bring new robotic platforms into them. ## What You Will Do - Integrate robotic hardware into autonomous laboratories, including robotic arms, mobile manipulators and humanoid platforms such as Dexmate, from initial arrival through running experiments under the laboratory orchestration software. - Write and extend device modules and REST APIs in MADSci, Argonne's open-source laboratory orchestration framework, so each new platform is controllable through the same interface as the rest of the laboratory. - Instrument robots with cameras, tactile and force sensors, and collect demonstration data using teleoperation devices. - Support the training and evaluation of policies built on large robotics foundation models. - Help determine which tasks are good candidates for learned policies and which are better served by conventional control. - Work closely with staff across robotics, software and the scientific domains supported by the laboratory. Contributions to MADSci will be released publicly. ## Position Requirements - Recently completed Bachelor's degree in computer science, robotics, electrical engineering, mechanical engineering or a related field. - Proficiency in Python, including writing code that others read and maintain. - Hands-on experience with robotic hardware, including setup, calibration and troubleshooting. - Experience with a robot middleware or control framework, such as ROS or ROS 2, or a vendor SDK. - Experience developing or consuming REST APIs. - Familiarity with Git and collaborative software development. - Demonstrated ability to bring up unfamiliar hardware from vendor documentation, and to make progress when that documentation is incomplete. - Ability to communicate clearly with colleagues from robotics, software and experimental science backgrounds. - Experience integrating sensors with robotic systems, including cameras, tactile sensors, or force and torque sensing. - Experience with teleoperation hardware and collecting demonstration data for robot learning. - Familiarity with machine learning frameworks such as PyTorch, and exposure to imitation learning, reinforcement learning or robotics foundation models. - Experience contributing to an open-source project. - Exposure to laboratory automation, scientific instrumentation or self-driving laboratories. - Experience with containerized deployment using Docker. - Ability to model Argonne's core values of impact, safety, respect, integrity and teamwork.