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of 10 493
pro vyhledávání: '"A. Mazzaglia"'
Object manipulation capabilities are essential skills that set apart embodied agents engaging with the world, especially in the realm of robotics. The ability to predict outcomes of interactions with objects is paramount in this setting. While model-
Externí odkaz:
http://arxiv.org/abs/2409.12005
Autor:
Grasso, Gianluca
Publikováno v:
Il Foro Italiano, 2016 Apr 01. 139(4), 1311/1312-1317/1318.
Externí odkaz:
https://www.jstor.org/stable/44875107
Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learning (RL) is hard to scale up as it requires a complex reward design for each task. In contrast, language can sp
Externí odkaz:
http://arxiv.org/abs/2406.18043
Joint space and task space control are the two dominant action modes for controlling robot arms within the robot learning literature. Actions in joint space provide precise control over the robot's pose, but tend to suffer from inefficient training;
Externí odkaz:
http://arxiv.org/abs/2406.04144
Robotic affordances, providing information about what actions can be taken in a given situation, can aid robotic manipulation. However, learning about affordances requires expensive large annotated datasets of interactions or demonstrations. In this
Externí odkaz:
http://arxiv.org/abs/2405.03865
Autor:
Trakadas, Athena
Publikováno v:
Trakadas, A 2022, ' Review of: D. Bernal-Casasola, D. Malfitana, A. Mazzaglia, J.J. Díaz, Le cetariae ellenistiche e romane di Portopalo (Sicilia). Primi resultati da ricerche interdisciplinari / Las cetariae helenísticas y romanas de Portopalo (Sicilia). Primeros apuntes interdisciplinares ', Journal of Maritime Archaeology, vol. 17, no. 3 . https://doi.org/10.1007/s11457-022-09340-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4a06bedbf8fe892a04c1619467eb3eb
https://doi.org/10.1007/s11457-022-09340-5
https://doi.org/10.1007/s11457-022-09340-5
Learning to navigate unknown environments from scratch is a challenging problem. This work presents a system that integrates world models with curiosity-driven exploration for autonomous navigation in new environments. We evaluate performance through
Externí odkaz:
http://arxiv.org/abs/2308.15852
Robotic affordances, providing information about what actions can be taken in a given situation, can aid robotic manipulation. However, learning about affordances requires expensive large annotated datasets of interactions or demonstrations. In this
Externí odkaz:
http://arxiv.org/abs/2308.14915
Understanding the world in terms of objects and the possible interplays with them is an important cognition ability, especially in robotics manipulation, where many tasks require robot-object interactions. However, learning such a structured world mo
Externí odkaz:
http://arxiv.org/abs/2307.02427
When deploying artificial agents in real-world environments where they interact with humans, it is crucial that their behavior is aligned with the values, social norms or other requirements of that environment. However, many environments have implici
Externí odkaz:
http://arxiv.org/abs/2305.02857