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Vedant Dave Under Construction Text |
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Research Interesttext |
Publications(* indicates equal contribution) |
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M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation
Fotios Lygerakis, Vedant Dave, Elmar Rueckert arXiv 2024 project page / paper M2CURL builds on MViTac to improves RL by efficiently integrating visual and tactile representations. It accelerates learning in downstream manipulation tasks. |
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Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training
Vedant Dave*, Fotios Lygerakis*, Elmar Rueckert ICRA 2024 project page / code / paper MViTac integrates vision and tactile modalities using contrastive learning, focusing on their inter and intra-modality relationships. |
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Can we infer the full-arm manipulation skills from tactile targets?
Vedant Dave, Elmar Rueckert Workshop on Advances in Close Proximity Human-Robot Collaboration, Humanoids 2022 paper Using a multi-image diffusion model as a regularizer lets you recover high-quality radiance fields from just a handful of images. |
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Predicting full-arm grasping motions from anticipated tactile responses
Vedant Dave, Elmar Rueckert Humanoids 2022 (Oral Presentation) paper TacProMPs learns and predicts complex arm movements based on tactile responses, particularly for manipulating non-uniform objects, demonstrating adaptability diverse grasping scenarios. |
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Orientation Probabilistic Movement Primitives on Riemannian Manifolds
Leonel Rozo*, Vedant Dave* CoRL 2021 paper Using a multi-image diffusion model as a regularizer lets you recover high-quality radiance fields from just a handful of images. |
Industrial Collaborations |
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