Zobrazeno 1 - 10
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pro vyhledávání: '"Röfer, Adrian"'
Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recent resurgence of object-centric approaches has demonstrated improved sa
Externí odkaz:
http://arxiv.org/abs/2411.03408
Humans seemingly incorporate potential touch signals in their perception. Our goal is to equip robots with a similar capability, which we term Imagine2touch. Imagine2touch aims to predict the expected touch signal based on a visual patch representing
Externí odkaz:
http://arxiv.org/abs/2405.01192
Humans seemingly incorporate potential touch signals in their perception. Our goal is to equip robots with a similar capability, which we term \ourmodel. \ourmodel aims to predict the expected touch signal based on a visual patch representing the tou
Externí odkaz:
http://arxiv.org/abs/2403.15107
Sample efficient learning of manipulation skills poses a major challenge in robotics. While recent approaches demonstrate impressive advances in the type of task that can be addressed and the sensing modalities that can be incorporated, they still re
Externí odkaz:
http://arxiv.org/abs/2403.14305
From dishwashers to cabinets, humans interact with articulated objects every day, and for a robot to assist in common manipulation tasks, it must learn a representation of articulation. Recent deep learning learning methods can provide powerful visio
Externí odkaz:
http://arxiv.org/abs/2309.16343
Setting up robot environments to quickly test newly developed algorithms is still a difficult and time consuming process. This presents a significant hurdle to researchers interested in performing real-world robotic experiments. RobotIO is a python l
Externí odkaz:
http://arxiv.org/abs/2207.13591
As robotic systems become more and more capable of assisting humans in their everyday lives, we must consider the opportunities for these artificial agents to make their human collaborators feel unsafe or to treat them unfairly. Robots can exhibit an
Externí odkaz:
http://arxiv.org/abs/2202.02654
Autor:
Nematollahi, Iman, Rosete-Beas, Erick, Röfer, Adrian, Welschehold, Tim, Valada, Abhinav, Burgard, Wolfram
A core challenge for an autonomous agent acting in the real world is to adapt its repertoire of skills to cope with its noisy perception and dynamics. To scale learning of skills to long-horizon tasks, robots should be able to learn and later refine
Externí odkaz:
http://arxiv.org/abs/2111.13129
Publikováno v:
IEEE Robotics and Automation Letters, 7 (2022) 3372-3379
Service robots in the future need to execute abstract instructions such as "fetch the milk from the fridge". To translate such instructions into actionable plans, robots require in-depth background knowledge. With regards to interactions with doors a
Externí odkaz:
http://arxiv.org/abs/2012.05362
We aim for mobile robots to function in a variety of common human environments. Such robots need to be able to reason about the locations of previously unseen target objects. Landmark objects can help this reasoning by narrowing down the search space
Externí odkaz:
http://arxiv.org/abs/2006.10807