Robot control adapts feedforward actions for new payloads automatically
Payload generalization on feedforward control actions for trajectory tracking in robots with rigid and elastic joints
Robotics
Summary
Robots do repetitive tasks well when they know how their parts move and how heavy their loads are. But it’s hard to know exactly how a robot with flexible parts moves or how a different load changes its movements. The authors created a way for robots to adjust their pre-planned movements when the load changes, without needing to know the exact robot details. This method works even for robots with flexible joints and lets them repeat the same motions smoothly with different objects.
What this means in practice
- •For industrial robot operators: Adjust robot control to maintain trajectory accuracy when payloads vary without reprogramming the controller.
- •For robot system integrators: Implement feedforward control algorithms that generalize across payloads for robots with both rigid and flexible joints.
Authors
Giorgia Pazzi, Giorgio Simonini, Michele Pierallini, Paolo Salaris, Giorgio Grioli, Manolo Garabini, Franco Angelini
Abstract
Industries employ robots for repetitive tasks due to their high precision and performance, typically achieved through feedforward control actions based on an accurate robot description. However, an exact dynamic model can be difficult to obtain, especially for robots with elastic elements or when the payload changes. Learning-based feedforward approaches are promising for trajectory tracking under model uncertainties, but typically face generalization issues when task parameters vary. This paper proposes a novel method to generalize the feedforward control action with respect to payload parameters, independently of the underlying controller. Firstly, the minimal number of example objects needed for payload generalization is derived. Then, an algorithm is proposed to execute the same trajectory with a new payload, given only its parameters, without knowledge of the robot's dynamic model and even in the presence of elastic joints. The effectiveness of the proposed algorithm is validated in simulations and experiments on tracking tasks with different robots and controllers. Two sets of experiments are performed: on a standard Franka Emika Panda robot, and on a Franka Emika Panda robot equipped with an elastic wrist.