Zobrazeno 1 - 10
of 8 394
pro vyhledávání: '"Atanasov, P."'
In this paper, we derive a new Kalman filter with probabilistic data association between measurements and states. We formulate a variational inference problem to approximate the posterior density of the state conditioned on the measurement data. We v
Externí odkaz:
http://arxiv.org/abs/2411.06378
Control Strategies for Pursuit-Evasion Under Occlusion Using Visibility and Safety Barrier Functions
Autor:
Zhou, Minnan, Shaikh, Mustafa, Chaubey, Vatsalya, Haggerty, Patrick, Koga, Shumon, Panagou, Dimitra, Atanasov, Nikolay
This paper develops a control strategy for pursuit-evasion problems in environments with occlusions. We address the challenge of a mobile pursuer keeping a mobile evader within its field of view (FoV) despite line-of-sight obstructions. The signed di
Externí odkaz:
http://arxiv.org/abs/2411.01321
Autor:
Nies, L., Atanasov, D., Athanasakis-Kaklamanakis, M., Au, M., Bernerd, C., Blaum, K., Chrysalidis, K., Fischer, P., Heinke, R., Klink, C., Lange, D., Lunney, D., Manea, V., Marsh, B. A., Müller, M., Mougeot, M., Naimi, S., Schweiger, Ch., Schweikhard, L., Wienholtz, F.
Mass measurements with the ISOLTRAP mass spectrometer at CERN-ISOLDE improve mass uncertainties of neutron-deficient tin isotopes towards doubly-magic $^{100}$Sn. The mass uncertainty of $^{103}$Sn was reduced by a factor of 4, and the new value for
Externí odkaz:
http://arxiv.org/abs/2410.17995
In this work, we introduce a planning neural operator (PNO) for predicting the value function of a motion planning problem. We recast value function approximation as learning a single operator from the cost function space to the value function space,
Externí odkaz:
http://arxiv.org/abs/2410.17547
We consider neural networks (NNs) where the final layer is down-scaled by a fixed hyperparameter $\gamma$. Recent work has identified $\gamma$ as controlling the strength of feature learning. As $\gamma$ increases, network evolution changes from "laz
Externí odkaz:
http://arxiv.org/abs/2410.04642
We develop a solvable model of neural scaling laws beyond the kernel limit. Theoretical analysis of this model shows how performance scales with model size, training time, and the total amount of available data. We identify three scaling regimes corr
Externí odkaz:
http://arxiv.org/abs/2409.17858
Autor:
Long, Kehan, Parwana, Hardik, Fainekos, Georgios, Hoxha, Bardh, Okamoto, Hideki, Atanasov, Nikolay
This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Euclidean Distance Function (N-CEDF). By learning separate distance fields for each link and combining them through the kinematics chain, the lea
Externí odkaz:
http://arxiv.org/abs/2409.13865
Autor:
Rosati, Domenic, Edkins, Giles, Raj, Harsh, Atanasov, David, Majumdar, Subhabrata, Rajendran, Janarthanan, Rudzicz, Frank, Sajjad, Hassan
While there has been progress towards aligning Large Language Models (LLMs) with human values and ensuring safe behaviour at inference time, safety-aligned LLMs are known to be vulnerable to training-time attacks such as supervised fine-tuning (SFT)
Externí odkaz:
http://arxiv.org/abs/2409.12914
Autor:
Lee, Ki Myung Brian, Dai, Zhirui, Gentil, Cedric Le, Wu, Lan, Atanasov, Nikolay, Vidal-Calleja, Teresa
We consider the problem of planning collision-free trajectories on distance fields. Our key observation is that querying a distance field at one configuration reveals a region of safe space whose radius is given by the distance value, obviating the n
Externí odkaz:
http://arxiv.org/abs/2408.13377
This paper proposes a control design approach for stabilizing nonlinear control systems. Our key observation is that the set of points where the decrease condition of a control Lyapunov function (CLF) is feasible can be regarded as a safe set. By lev
Externí odkaz:
http://arxiv.org/abs/2408.08398