Zobrazeno 1 - 10
of 14 473
pro vyhledávání: '"A, Kober"'
Autor:
Montero, Mariano Ramírez, Shahabi, Ebrahim, Franzese, Giovanni, Kober, Jens, Mazzolai, Barbara, Della Santina, Cosimo
Soft robots have the potential to revolutionize the use of robotic systems with their capability of establishing safe, robust, and adaptable interactions with their environment, but their precise control remains challenging. In contrast, traditional
Externí odkaz:
http://arxiv.org/abs/2410.07787
Real-world environments require robots to continuously acquire new skills while retaining previously learned abilities, all without the need for clearly defined task boundaries. Storing all past data to prevent forgetting is impractical due to storag
Externí odkaz:
http://arxiv.org/abs/2410.02995
Reinforcement learning-based quadruped robots excel across various terrains but still lack the ability to swim in water due to the complex underwater environment. This paper presents the development and evaluation of a data-driven hydrodynamic model
Externí odkaz:
http://arxiv.org/abs/2410.00490
Autor:
Vanc, Petr, Franzese, Giovanni, Behrens, Jan Kristof, Della Santina, Cosimo, Stepanova, Karla, Kober, Jens
Learning from demonstration is a promising way of teaching robots new skills. However, a central problem when executing acquired skills is to recognize risks and failures. This is essential since the demonstrations usually cover only a few mostly suc
Externí odkaz:
http://arxiv.org/abs/2409.20173
Autor:
Hosseini, Kasra, Kober, Thomas, Krapac, Josip, Vollgraf, Roland, Cheng, Weiwei, Ramallo, Ana Peleteiro
Evaluating production-level retrieval systems at scale is a crucial yet challenging task due to the limited availability of a large pool of well-trained human annotators. Large Language Models (LLMs) have the potential to address this scaling issue a
Externí odkaz:
http://arxiv.org/abs/2409.11860
Planning methods struggle with computational intractability in solving task-level problems in large-scale environments. This work explores leveraging the commonsense knowledge encoded in LLMs to empower planning techniques to deal with these complex
Externí odkaz:
http://arxiv.org/abs/2409.04775
Large language models (LLMs) are poised to revolutionize the domain of online fashion retail, enhancing customer experience and discovery of fashion online. LLM-powered conversational agents introduce a new way of discovery by directly interacting wi
Externí odkaz:
http://arxiv.org/abs/2408.08907
Autor:
Owens, C. Braxton, Mathew, Nithin, Olaveson, Tyce W., Tavenner, Jacob P., Kober, Edward M., Tucker, Garritt J., Hart, Gus L. W., Homer, Eric R.
Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to recent efforts to obtain these relationships with machine learning, but
Externí odkaz:
http://arxiv.org/abs/2407.21228
Autor:
Aprile, Manuel, Fiorini, Samuel, Joret, Gwenaël, Kober, Stefan, Seweryn, Michał T., Weltge, Stefan, Yuditsky, Yelena
It is a notorious open question whether integer programs (IPs), with an integer coefficient matrix $M$ whose subdeterminants are all bounded by a constant $\Delta$ in absolute value, can be solved in polynomial time. We answer this question in the af
Externí odkaz:
http://arxiv.org/abs/2407.09477
Sim2real, that is, the transfer of learned control policies from simulation to real world, is an area of growing interest in robotics due to its potential to efficiently handle complex tasks. The sim2real approach faces challenges due to mismatches b
Externí odkaz:
http://arxiv.org/abs/2407.04328