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

03/06/2022
by   Rui Chen, et al.
0

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.

READ FULL TEXT
research
08/04/2021

Tolerance-Guided Policy Learning for Adaptable and Transferrable Delicate Industrial Insertion

Policy learning for delicate industrial insertion tasks (e.g., PC board ...
research
09/05/2023

A Lightweight and Transferable Design for Robust LEGO Manipulation

LEGO is a well-known platform for prototyping pixelized objects. However...
research
03/05/2021

Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms

Reinforcement learning methods can achieve significant performance but r...
research
03/17/2023

Zero-shot Transferable and Persistently Feasible Safe Control for High Dimensional Systems by Consistent Abstraction

Safety is critical in robotic tasks. Energy function based methods have ...
research
03/12/2023

Certifiably-correct Control Policies for Safe Learning and Adaptation in Assistive Robotics

Guaranteeing safety in human-centric applications is critical in robot l...
research
10/04/2022

Safely Learning Visuo-Tactile Feedback Policies in Real For Industrial Insertion

Industrial insertion tasks are often performed repetitively with parts t...
research
03/05/2020

SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks

Machine learning techniques have been widely applied in Internet compani...

Please sign up or login with your details

Forgot password? Click here to reset