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Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
Aalto Univ, Dept Elect Engn & Automat, Intelligent Robot Grp, Helsinki, Finland..
KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.ORCID-id: 0000-0001-9603-1677
Orebro Univ, AASS Res Ctr, Orebro, Sweden..
Orebro Univ, AASS Res Ctr, Orebro, Sweden..
Vise andre og tillknytning
2018 (engelsk)Inngår i: 2018 IEEE-RAS 18TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS (HUMANOIDS) / [ed] Asfour, T, Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 132-138Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2018. s. 132-138
Serie
IEEE-RAS International Conference on Humanoid Robots, ISSN 2164-0572
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-245097DOI: 10.1109/HUMANOIDS.2018.8624948ISI: 000458689700019Scopus ID: 2-s2.0-85062286430ISBN: 978-1-5386-7283-9 (tryckt)OAI: oai:DiVA.org:kth-245097DiVA, id: diva2:1294613
Konferanse
18th IEEE-RAS International Conference on Humanoid Robots (Humanoids), NOV 06-09, 2018, Beijing Inst Technol, Beijing, PEOPLES R CHINA
Merknad

QC 20190308

Tilgjengelig fra: 2019-03-08 Laget: 2019-03-08 Sist oppdatert: 2019-04-11bibliografisk kontrollert

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