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pro vyhledávání: '"Fazeli A"'
Reconstructing unknown external source functions is an important perception capability for a large range of robotics domains including manipulation, aerial, and underwater robotics. In this work, we propose a Physics-Informed Neural Network (PINN [1]
Externí odkaz:
http://arxiv.org/abs/2411.01665
Autor:
Rodriguez, Samanta, Dou, Yiming, Bogert, William van den, Oller, Miquel, So, Kevin, Owens, Andrew, Fazeli, Nima
Today's tactile sensors have a variety of different designs, making it challenging to develop general-purpose methods for processing touch signals. In this paper, we learn a unified representation that captures the shared information between differen
Externí odkaz:
http://arxiv.org/abs/2410.11834
Tactile sensing provides robots with rich feedback during manipulation, enabling a host of perception and controls capabilities. Here, we present a new open-source, vision-based tactile sensor designed to promote reproducibility and accessibility acr
Externí odkaz:
http://arxiv.org/abs/2409.19770
Tactile sensing is a powerful means of implicit communication between a human and a robot assistant. In this paper, we investigate how tactile sensing can transcend cross-embodiment differences across robotic systems in the context of collaborative m
Externí odkaz:
http://arxiv.org/abs/2409.14896
In-hand object manipulation is an important capability for dexterous manipulation. In this paper, we introduce a modeling and planning framework for in-hand object reconfiguration, focusing on frictional patch contacts between the robot's palms (or f
Externí odkaz:
http://arxiv.org/abs/2409.14698
Developing robust and correctable visuomotor policies for robotic manipulation is challenging due to the lack of self-recovery mechanisms from failures and the limitations of simple language instructions in guiding robot actions. To address these iss
Externí odkaz:
http://arxiv.org/abs/2409.14674
Estimating contact locations between a grasped object and the environment is important for robust manipulation. In this paper, we present a visual-auditory method for extrinsic contact estimation, featuring a real-to-sim approach for auditory signals
Externí odkaz:
http://arxiv.org/abs/2409.14608
Modern incarnations of tactile sensors produce high-dimensional raw sensory feedback such as images, making it challenging to efficiently store, process, and generalize across sensors. To address these concerns, we introduce a novel implicit function
Externí odkaz:
http://arxiv.org/abs/2409.14592
Tactile sensing has proven to be an invaluable tool for enhancing robotic perception, particularly in scenarios where visual data is limited or unavailable. However, traditional methods for pose estimation using tactile data often rely on intricate m
Externí odkaz:
http://arxiv.org/abs/2409.13923
Today's touch sensors come in many shapes and sizes. This has made it challenging to develop general-purpose touch processing methods since models are generally tied to one specific sensor design. We address this problem by performing cross-modal pre
Externí odkaz:
http://arxiv.org/abs/2409.08269