Learning Stable Normalizing-Flow Control for Robotic Manipulation

Abdul Khader, Shahbaz (author): KTH,Robotik, perception och lärande, RPL,ABB Corp Res, Västerås, Sweden.

(creator_code:org_t)

Institute of Electrical and Electronics Engineers (IEEE), 2021
2021
English.
In: 2021 IEEE International Conference On Robotics And Automation (ICRA 2021). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1644-1650

Related links:: https://urn.kb.se/re...; show more...; https://doi.org/10.1...; show less...

Abstract Subject headings

Reinforcement Learning (RL) of robotic manipulation skills, despite its impressive successes, stands to benefit from incorporating domain knowledge from control theory. One of the most important properties that is of interest is control stability. Ideally, one would like to achieve stability guarantees while staying within the framework of state-of-the-art deep RL algorithms. Such a solution does not exist in general, especially one that scales to complex manipulation tasks. We contribute towards closing this gap by introducing normalizing-flow control structure, that can be deployed in any latest deep RL algorithms. While stable exploration is not guaranteed, our method is designed to ultimately produce deterministic controllers with provable stability. In addition to demonstrating our method on challenging contact-rich manipulation tasks, we also show that it is possible to achieve considerable exploration efficiency-reduced state space coverage and actuation efforts- without losing learning efficiency.

By the author/editor: Abdul Khader, Sh ...; Yin, Hang; Falco, Pietro; Kragic, Danica, ...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Robotics

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.