M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation

Fotios Lygerakis; Vedant Dave; Elmar Rückert

M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation

Fotios Lygerakis, Vedant Dave, Elmar Rückert

Lehrstuhl für Cyber Physical Systems (190)

Publikation: Beitrag in Fachzeitschrift › Konferenzartikel › Begutachtung

Originalsprache	Englisch
Fachzeitschrift	IEEE International Conference on Ubiquitous Robots
Publikationsstatus	Veröffentlicht - 1 März 2024

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https://cloud.cps.unileoben.ac.at/index.php/s/NPejb2Fp4Y8LeyZ

Learning Robust Multimodal Representations under Noisy Conditions
Vedant Dave (Redner)
20 Mai 2024
Aktivität: Gespräch oder Vortrag › Mitwirkung als Gastredner/-in

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title = "M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation",

keywords = "Contrastive Learning, Manipulation, Multimodal Reinforcement Learning, Multimodal Representation Learning, Reinforcement Learning, Robot Learning",

author = "Fotios Lygerakis and Vedant Dave and Elmar R{\"u}ckert",

year = "2024",

month = mar,

day = "1",

language = "English",

journal = "IEEE International Conference on Ubiquitous Robots",

publisher = "IEEE Press",

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TY - JOUR

T1 - M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation

AU - Lygerakis, Fotios

AU - Dave, Vedant

AU - Rückert, Elmar

PY - 2024/3/1

Y1 - 2024/3/1

KW - Contrastive Learning

KW - Manipulation

KW - Multimodal Reinforcement Learning

KW - Multimodal Representation Learning

KW - Reinforcement Learning

KW - Robot Learning

M3 - Conference article

JO - IEEE International Conference on Ubiquitous Robots

JF - IEEE International Conference on Ubiquitous Robots

ER -

M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation

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Aktivitäten

Learning Robust Multimodal Representations under Noisy Conditions

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