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pro vyhledávání: '"Garrabe, Emiland"'
Autor:
Appius, Aurel X., Garrabe, Emiland, Helenon, Francois, Khoramshahi, Mahdi, Doncieux, Stephane
Task-aware robotic grasping is a challenging problem that requires the integration of semantic understanding and geometric reasoning. Traditional grasp planning approaches focus on stable or feasible grasps, often disregarding the specific tasks the
Externí odkaz:
http://arxiv.org/abs/2411.14917
Recent advances in large language models (LLMs) have led to significant progress in robotics, enabling embodied agents to better understand and execute open-ended tasks. However, existing approaches using LLMs face limitations in grounding their outp
Externí odkaz:
http://arxiv.org/abs/2411.05474
This paper is concerned with a finite-horizon inverse control problem, which has the goal of reconstructing, from observations, the possibly non-convex and non-stationary cost driving the actions of an agent. In this context, we present a result enab
Externí odkaz:
http://arxiv.org/abs/2306.13928
We consider the problem of estimating the possibly non-convex cost of an agent by observing its interactions with a nonlinear, non-stationary and stochastic environment. For this inverse problem, we give a result that allows to estimate the cost by s
Externí odkaz:
http://arxiv.org/abs/2303.17957
We consider the problem of designing agents able to compute optimal decisions by composing data from multiple sources to tackle tasks involving: (i) tracking a desired behavior while minimizing an agent-specific cost; (ii) satisfying safety constrain
Externí odkaz:
http://arxiv.org/abs/2303.13315
Autor:
Garrabé, Émiland, Russo, Giovanni
We present the principled design of CRAWLING: a CRowdsourcing Algorirthm on WheeLs for smart parkING. CRAWLING is an in-car service for the routing of connected cars. Specifically, cars equipped with our service are able to {\em crowdsource} data fro
Externí odkaz:
http://arxiv.org/abs/2212.02467
Autor:
Garrabe, Emiland, Russo, Giovanni
Publikováno v:
Annual Reviews in Control, Vol. 54, 2022, Pages 81-102
This survey is focused on certain sequential decision-making problems that involve optimizing over probability functions. We discuss the relevance of these problems for learning and control. The survey is organized around a framework that combines a
Externí odkaz:
http://arxiv.org/abs/2201.05212
Autor:
Garrabé, Émiland, Russo, Giovanni
This paper is concerned with the problem of designing agents able to dynamically select information from multiple data sources in order to tackle tasks that involve tracking a target behavior while optimizing a reward. We formulate this problem as a
Externí odkaz:
http://arxiv.org/abs/2103.02020
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