Paper:
Many factories use robots, but each automation project is far more complex (and therefore costly) than it might initially appear. Current manipulators are primarily designed to repeat trajectories without variation or adaptation. For each workstation, engineers have three levers for automation: (1) limiting variability — for example, the position of objects to grasp — using accessories such as vibratory bowls and guides, (2) developing specialized vision-based algorithms to perceive this variability, and (3) developing task-specific grippers for each object (typically suction-based). Beyond hardware costs, this development process requires substantial engineering investment. This expense is only justified for tasks with high production volumes and minimal changes (product changes, process modifications, etc.). These automation costs explain why, to our knowledge, only a small percentage of tasks are fully automated in French & German factories.
Our analysis of industrial needs has clearly identified the value of "universal" robots that can be easily configured for many applications and replace a worker without requiring workstation modifications. The typical scenario we are targeting involves a bimanual wheeled robot (humanoid capabilities: mobility and bimanual manipulation) that learns a task either through teleoperation with a joystick or using manual grippers (see study 1), then reproduces it from demonstrations. A common factory example is machine loading: a worker must grasp a part from a bin or box, place it in a machine (e.g., a milling machine, laser engraver, or press), close the machine, press a button, wait for the result, then grasp the part, and position it in a new box. Typically, a factory operates several slightly different machines (different generations) and may change object types and sequences frequently based on orders (e.g., monthly). The critical scientific challenge is enabling robot training from demonstrations alone, as naturally as possible—without needing to manually sequence subtasks, scan objects for 3D meshes, train object classifiers, etc., as is currently required to automate a production line. In other words, the goal is to reduce automation costs to hardware and software costs only. This is fundamentally an imitation learning problem.
These tasks typically involve variability because they are des