Zobrazeno 1 - 10
of 9 991
pro vyhledávání: '"Atanasov, At"'
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
Recent years have seen substantial advances in our understanding of high-dimensional ridge regression, but existing theories assume that training examples are independent. By leveraging recent techniques from random matrix theory and free probability
Externí odkaz:
http://arxiv.org/abs/2408.04607
Achieving both target accuracy and robustness in dynamic maneuvers with long flight phases, such as high or long jumps, has been a significant challenge for legged robots. To address this challenge, we propose a novel learning-based control approach
Externí odkaz:
http://arxiv.org/abs/2407.14749
Autor:
Liu, Xu, Lei, Jiuzhou, Prabhu, Ankit, Tao, Yuezhan, Spasojevic, Igor, Chaudhari, Pratik, Atanasov, Nikolay, Kumar, Vijay
This paper develops a real-time decentralized metric-semantic Simultaneous Localization and Mapping (SLAM) approach that leverages a sparse and lightweight object-based representation to enable a heterogeneous robot team to autonomously explore 3D en
Externí odkaz:
http://arxiv.org/abs/2406.17249
This paper focuses on transferring control policies between robot manipulators with different morphology. While reinforcement learning (RL) methods have shown successful results in robot manipulation tasks, transferring a trained policy from simulati
Externí odkaz:
http://arxiv.org/abs/2406.01968
We introduce a novel method for safe mobile robot navigation in dynamic, unknown environments, utilizing onboard sensing to impose safety constraints without the need for accurate map reconstruction. Traditional methods typically rely on detailed map
Externí odkaz:
http://arxiv.org/abs/2405.18251
Autor:
Rosati, Domenic, Wehner, Jan, Williams, Kai, Bartoszcze, Łukasz, Atanasov, David, Gonzales, Robie, Majumdar, Subhabrata, Maple, Carsten, Sajjad, Hassan, Rudzicz, Frank
Releasing open-source large language models (LLMs) presents a dual-use risk since bad actors can easily fine-tune these models for harmful purposes. Even without the open release of weights, weight stealing and fine-tuning APIs make closed models vul
Externí odkaz:
http://arxiv.org/abs/2405.14577
This paper presents a succinct derivation of the training and generalization performance of a variety of high-dimensional ridge regression models using the basic tools of random matrix theory and free probability. We provide an introduction and revie
Externí odkaz:
http://arxiv.org/abs/2405.00592
Multi-robot simultaneous localization and mapping (SLAM) enables a robot team to achieve coordinated tasks by relying on a common map of the environment. Constructing a map by centralized processing of the robot observations is undesirable because it
Externí odkaz:
http://arxiv.org/abs/2404.18331