Zobrazeno 1 - 10
of 6 075
pro vyhledávání: '"Peters, Jan"'
Autor:
Prasad, Vignesh, Kshirsagar, Alap, Koert, Dorothea, Stock-Homburg, Ruth, Peters, Jan, Chalvatzaki, Georgia
Shared dynamics models are important for capturing the complexity and variability inherent in Human-Robot Interaction (HRI). Therefore, learning such shared dynamics models can enhance coordination and adaptability to enable successful reactive inter
Externí odkaz:
http://arxiv.org/abs/2407.07636
Autor:
Nguyen, Duy M. H., Le, An T., Nguyen, Trung Q., Diep, Nghiem T., Nguyen, Tai, Duong-Tran, Duy, Peters, Jan, Shen, Li, Niepert, Mathias, Sonntag, Daniel
Prompt learning methods are gaining increasing attention due to their ability to customize large vision-language models to new domains using pre-trained contextual knowledge and minimal training data. However, existing works typically rely on optimiz
Externí odkaz:
http://arxiv.org/abs/2407.04489
Planning robot contact often requires reasoning over a horizon to anticipate outcomes, making such planning problems computationally expensive. In this letter, we propose a learning framework for efficient contact planning in real-time subject to unc
Externí odkaz:
http://arxiv.org/abs/2407.03705
Autor:
Mower, Christopher E., Wan, Yuhui, Yu, Hongzhan, Grosnit, Antoine, Gonzalez-Billandon, Jonas, Zimmer, Matthieu, Wang, Jinlong, Zhang, Xinyu, Zhao, Yao, Zhai, Anbang, Liu, Puze, Palenicek, Daniel, Tateo, Davide, Cadena, Cesar, Hutter, Marco, Peters, Jan, Tian, Guangjian, Zhuang, Yuzheng, Shao, Kun, Quan, Xingyue, Hao, Jianye, Wang, Jun, Bou-Ammar, Haitham
We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our system integrates large language models (LLMs), enabling non-experts to a
Externí odkaz:
http://arxiv.org/abs/2406.19741
Deep Reinforcement Learning (RL) is well known for being highly sensitive to hyperparameters, requiring practitioners substantial efforts to optimize them for the problem at hand. In recent years, the field of automated Reinforcement Learning (AutoRL
Externí odkaz:
http://arxiv.org/abs/2405.16195
Autor:
Palenicek, Daniel, Gruner, Theo, Schneider, Tim, Böhm, Alina, Lenz, Janis, Pfenning, Inga, Krämer, Eric, Peters, Jan
Humans have exceptional tactile sensing capabilities, which they can leverage to solve challenging, partially observable tasks that cannot be solved from visual observation alone. Research in tactile sensing attempts to unlock this new input modality
Externí odkaz:
http://arxiv.org/abs/2405.00383
Autor:
Becker, Noah, Gattung, Erik, Hansel, Kay, Schneider, Tim, Zhu, Yaonan, Hasegawa, Yasuhisa, Peters, Jan
Telerobotics enables humans to overcome spatial constraints and allows them to physically interact with the environment in remote locations. However, the sensory feedback provided by the system to the operator is often purely visual, limiting the ope
Externí odkaz:
http://arxiv.org/abs/2404.19585
Publikováno v:
2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), Austin, TX, USA, 2023
Clustering of motion trajectories is highly relevant for human-robot interactions as it allows the anticipation of human motions, fast reaction to those, as well as the recognition of explicit gestures. Further, it allows automated analysis of record
Externí odkaz:
http://arxiv.org/abs/2404.17269
Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. However, most existing approaches are trained in well-tuned simulators and subsequently de
Externí odkaz:
http://arxiv.org/abs/2404.09080
Autor:
Böhm, Alina, Schneider, Tim, Belousov, Boris, Kshirsagar, Alap, Lin, Lisa, Doerschner, Katja, Drewing, Knut, Rothkopf, Constantin A., Peters, Jan
This paper explores active sensing strategies that employ vision-based tactile sensors for robotic perception and classification of fabric textures. We formalize the active sampling problem in the context of tactile fabric recognition and provide an
Externí odkaz:
http://arxiv.org/abs/2403.13701