2025
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Vanjani, Pankhuri; Mattes, Paul; Li, Maximilian Xiling; Dave, Vedant; Lioutikov, Rudolf DisDP: Robust Imitation Learning via Disentangled Diffusion Policies Proceedings Article Forthcoming In: Reinforcement Learning Conference (RLC), Reinforcement Learning Journal, Forthcoming. @inproceedings{dave2025disdp,
title = {DisDP: Robust Imitation Learning via Disentangled Diffusion Policies},
author = {Pankhuri Vanjani and Paul Mattes and Maximilian Xiling Li and Vedant Dave and Rudolf Lioutikov},
year = {2025},
date = {2025-05-09},
urldate = {2025-05-09},
booktitle = {Reinforcement Learning Conference (RLC)},
publisher = {Reinforcement Learning Journal},
keywords = {Contrastive Learning, Imitation Learning, Representation Learning},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
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2024
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Lygerakis, Fotios; Dave, Vedant; Rueckert, Elmar M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation Proceedings Article In: IEEE International Conference on Ubiquitous Robots (UR 2024), IEEE 2024. @inproceedings{Lygerakis2024,
title = {M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation},
author = {Fotios Lygerakis and Vedant Dave and Elmar Rueckert},
url = {https://cloud.cps.unileoben.ac.at/index.php/s/NPejb2Fp4Y8LeyZ},
year = {2024},
date = {2024-04-04},
urldate = {2024-04-04},
booktitle = {IEEE International Conference on Ubiquitous Robots (UR 2024)},
organization = {IEEE},
keywords = {Contrastive Learning, Manipulation, Multimodal Reinforcement Learning, Multimodal Representation Learning, Reinforcement Learning, Robot Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
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Dave*, Vedant; Lygerakis*, Fotios; Rueckert, Elmar Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training Proceedings Article In: IEEE International Conference on Robotics and Automation (ICRA), pp. 8013-8020, IEEE, 2024, ISBN: 979-8-3503-8457-4, (* equal contribution). @inproceedings{Dave2024b,
title = {Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training},
author = {Vedant Dave* and Fotios Lygerakis* and Elmar Rueckert},
url = {https://cloud.cps.unileoben.ac.at/index.php/s/Nw9TprfdDoLgr8e},
doi = {10.1109/ICRA57147.2024.10610228},
isbn = {979-8-3503-8457-4},
year = {2024},
date = {2024-01-28},
urldate = {2024-01-28},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
pages = {8013-8020},
publisher = {IEEE},
abstract = {The rapidly evolving field of robotics necessitates methods that can facilitate the fusion of multiple modalities. Specifically, when it comes to interacting with tangible objects, effectively combining visual and tactile sensory data is key to understanding and navigating the complex dynamics of the physical world, enabling a more nuanced and adaptable response to changing environments. Nevertheless, much of the earlier work in merging these two sensory modalities has relied on supervised methods utilizing datasets labeled by humans. This paper introduces MViTac, a novel methodology that leverages contrastive learning to integrate vision and touch sensations in a self-supervised fashion. By availing both sensory inputs, MViTac leverages intra and inter-modality losses for learning representations, resulting in enhanced material property classification and more adept grasping prediction. Through a series of experiments, we showcase the effectiveness of our method and its superiority over existing state-of-the-art self-supervised and supervised techniques. In evaluating our methodology, we focus on two distinct tasks: material classification and grasping success prediction. Our results indicate that MViTac facilitates the development of improved modality encoders, yielding more robust representations as evidenced by linear probing assessments. https://sites.google.com/view/mvitac/home},
note = {* equal contribution},
keywords = {Contrastive Learning, Representation Learning, Self-supervised Learning, Tactile Sensing},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapidly evolving field of robotics necessitates methods that can facilitate the fusion of multiple modalities. Specifically, when it comes to interacting with tangible objects, effectively combining visual and tactile sensory data is key to understanding and navigating the complex dynamics of the physical world, enabling a more nuanced and adaptable response to changing environments. Nevertheless, much of the earlier work in merging these two sensory modalities has relied on supervised methods utilizing datasets labeled by humans. This paper introduces MViTac, a novel methodology that leverages contrastive learning to integrate vision and touch sensations in a self-supervised fashion. By availing both sensory inputs, MViTac leverages intra and inter-modality losses for learning representations, resulting in enhanced material property classification and more adept grasping prediction. Through a series of experiments, we showcase the effectiveness of our method and its superiority over existing state-of-the-art self-supervised and supervised techniques. In evaluating our methodology, we focus on two distinct tasks: material classification and grasping success prediction. Our results indicate that MViTac facilitates the development of improved modality encoders, yielding more robust representations as evidenced by linear probing assessments. https://sites.google.com/view/mvitac/home |  |
