A Composable Framework for Policy Design, Learning, and Transfer Toward Safe and Efficient Industrial Insertion

TL;DR

This paper introduces a modular, safe, and transferable framework for robotic insertion tasks that enhances safety, accuracy, robustness, and adaptability on existing hardware, demonstrated on a UR10 robot.

Contribution

It presents a novel composable framework with interpretable modules for safe, efficient, and transferable robotic insertion policies, addressing hardware constraints and task variability.

Findings

Achieves safety and precision in delicate insertion tasks.

Demonstrates robustness against perception errors and component defects.

Shows effective transferability and efficiency on UR10 robot.

Abstract

Delicate industrial insertion tasks (e.g., PC board assembly) remain challenging for industrial robots. The challenges include low error tolerance, delicacy of the components, and large task variations with respect to the components to be inserted. To deliver a feasible robotic solution for these insertion tasks, we also need to account for hardware limits of existing robotic systems and minimize the integration effort. This paper proposes a composable framework for efficient integration of a safe insertion policy on existing robotic platforms to accomplish these insertion tasks. The policy has an interpretable modularized design and can be learned efficiently on hardware and transferred to new tasks easily. In particular, the policy includes a safe insertion agent as a baseline policy for insertion, an optimal configurable Cartesian tracker as an interface to robot hardware, a probabilistic inference module to handle component variety and insertion errors, and a safe learning module to optimize the parameters in the aforementioned modules to achieve the best performance on designated hardware. The experiment results on a UR10 robot show that the proposed framework achieves safety (for the delicacy of components), accuracy (for low tolerance), robustness (against perception error and component defection), adaptability and transferability (for task variations), as well as task efficiency during execution plus data and time efficiency during learning.