Hierarchical learning of robotic contact policies

Simonič, Mihael (avtor), Ude, Aleš (avtor), Nemec, Bojan (avtor)

The paper addresses the issue of learning tasks where a robot maintains permanent contact with the environment. We propose a new methodology based on a hierarchical learning scheme coupled with task representation through directed graphs. These graphs are constituted of nodes and branches that correspond to the states and robotic actions, respectively. The upper level of the hierarchy essentially operates as a decision-making algorithm. It leverages reinforcement learning (RL) techniques to facilitate optimal decision-making. The actions are generated by a constraint-space following (CSF) controller that autonomously identifies feasible directions for motion. The controller generates robot motion by adjusting its stiffness in the direction defined by the Frenet–Serret frame, which is aligned with the robot path. The proposed framework was experimentally verified through a series of challenging robotic tasks such as maze learning, door opening, learning to shift the manual car gear, and learning car license plate light assembly by disassembly.

• Jezik: Angleški jezik
• Ključne besede: autonomous robot learning, learning, experience, compliance and impedance control, learning from experience, perception-action coupling, compliant assembly
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: 12 str.
• Številčenje: Vol. 86, art. 102657
• PID: 20.500.12556/RUL-153103
• UDK: 004
• ISSN pri članku: 1879-2537
• DOI: 10.1016/j.rcim.2023.102657
• COBISS.SI-ID: 164894211
• Datum objave v RUL: 18.12.2023

Gradivo je del revije

• Naslov: Robotics and computer-integrated manufacturing
• Založnik: Elsevier
• ISSN: 1879-2537
• COBISS.SI-ID: 18810902

Licence

• Licenca: CC BY-NC 4.0, Creative Commons Priznanje avtorstva-Nekomercialno 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc/4.0/deed.sl
• Opis: Licenca Creative Commons, ki prepoveduje komercialno uporabo, vendar uporabniki ne rabijo upravljati materialnih avtorskih pravic na izpeljanih delih z enako licenco.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, učenje, izkušnje

Projekti

• Financer: EC - European Commission
• Program financ.: H2020
• Številka projekta: 871352
• Naslov: Self-reconfiguration of a robotic workcell for the recycling of electronic waste
• Akronim: ReconCycle

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0076
• Naslov: Avtomatika, robotika in biokibernetika