Assembly robots with optimized control stiffness through reinforcement learning

02/27/2020
by   Masahide Oikawa, et al.
0

There is an increased demand for task automation in robots. Contact-rich tasks, wherein multiple contact transitions occur in a series of operations, are extensively being studied to realize high accuracy. In this study, we propose a methodology that uses reinforcement learning (RL) to achieve high performance in robots for the execution of assembly tasks that require precise contact with objects without causing damage. The proposed method ensures the online generation of stiffness matrices that help improve the performance of local trajectory optimization. The method has an advantage of rapid response owing to short sampling time of the trajectory planning. The effectiveness of the method was verified via experiments involving two contact-rich tasks. The results indicate that the proposed method can be implemented in various contact-rich manipulations. A demonstration video shows the performance. (https://youtu.be/gxSCl7Tp4-0)

READ FULL TEXT

page 4

page 7

research
08/30/2020

Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives

In recent years, industrial robots have been installed in various indust...
research
05/26/2023

IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality

Robotic assembly is a longstanding challenge, requiring contact-rich int...
research
02/02/2022

Towards High-Payload Admittance Control for Manual Guidance with Environmental Contact

Force control enables hands-on teaching and physical collaboration, with...
research
03/10/2021

Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Tasks

Collaborative robots are expected to be able to work alongside humans an...
research
11/23/2020

COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing

General contact-rich manipulation problems are long-standing challenges ...
research
08/12/2022

Maximizing the Use of Environmental Constraints: A Pushing-Based Hybrid Position/Force Assembly Skill for Contact-Rich Tasks

The need for contact-rich tasks is rapidly growing in modern manufacturi...
research
10/11/2022

A Learning-Based Estimation and Control Framework for Contact-Intensive Tight-Tolerance Tasks

We propose a novel data-driven estimation and control framework for cont...

Please sign up or login with your details

Forgot password? Click here to reset