Zobrazeno 1 - 10
of 7 745
pro vyhledávání: '"P, Ghaffari"'
Autor:
Wilson, Joey, Almeida, Marcelino, Sun, Min, Mahajan, Sachit, Ghaffari, Maani, Ewen, Parker, Ghasemalizadeh, Omid, Kuo, Cheng-Hao, Sen, Arnie
In this paper, we present a novel algorithm for probabilistically updating and rasterizing semantic maps within 3D Gaussian Splatting (3D-GS). Although previous methods have introduced algorithms which learn to rasterize features in 3D-GS for enhance
Externí odkaz:
http://arxiv.org/abs/2411.02547
Tensegrity robots, characterized by a synergistic assembly of rigid rods and elastic cables, form robust structures that are resistant to impacts. However, this design introduces complexities in kinematics and dynamics, complicating control and state
Externí odkaz:
http://arxiv.org/abs/2410.24226
Autor:
Ghaffari, Mohsen, Grunau, Christoph
A recent work by Christiansen, Nowicki, and Rotenberg provides dynamic algorithms for coloring sparse graphs, concretely as a function of the arboricity alpha of the input graph. They give two randomized algorithms: O({alpha} log {alpha}) implicit co
Externí odkaz:
http://arxiv.org/abs/2410.19536
Autor:
Ghaffari, Mohsen, Grunau, Christoph
This paper improves and in two cases nearly settles, up to logarithmically lower-order factors, the deterministic complexity of some of the most central problems in distributed graph algorithms, which have been studied for over three decades: Near-Op
Externí odkaz:
http://arxiv.org/abs/2410.19516
Autor:
Schankula, Christopher William, Hadigheh, Habib Ghaffari, Smith, Spencer, Anand, Christopher Kumar
Design skills are increasingly recognized as a core competency for software professionals. Unfortunately, these skills are difficult to teach because design requires freedom and open-ended thinking, but new designers require a structured process to k
Externí odkaz:
http://arxiv.org/abs/2410.12120
Autor:
Wilson, Joey, Xu, Ruihan, Sun, Yile, Ewen, Parker, Zhu, Minghan, Barton, Kira, Ghaffari, Maani
This paper introduces a novel probabilistic mapping algorithm, Latent BKI, which enables open-vocabulary mapping with quantifiable uncertainty. Traditionally, semantic mapping algorithms focus on a fixed set of semantic categories which limits their
Externí odkaz:
http://arxiv.org/abs/2410.11783
Autor:
Teng, Sangli, Iwasaki, Kaito, Clark, William, Yu, Xihang, Bloch, Anthony, Vasudevan, Ram, Ghaffari, Maani
This work generalizes the classical metriplectic formalism to model Hamiltonian systems with nonconservative dissipation. Classical metriplectic representations allow for the description of energy conservation and production of entropy via a suitable
Externí odkaz:
http://arxiv.org/abs/2410.06233
This paper introduces a Multi-modal Diffusion model for Motion Prediction (MDMP) that integrates and synchronizes skeletal data and textual descriptions of actions to generate refined long-term motion predictions with quantifiable uncertainty. Existi
Externí odkaz:
http://arxiv.org/abs/2410.03860
Autor:
Song, Jingwei, Ghaffari, Maani
This paper addresses a special Perspective-n-Point (PnP) problem: estimating the optimal pose to align 3D and 2D shapes in real-time without correspondences, termed as correspondence-free PnP. While several studies have focused on 3D and 2D shape reg
Externí odkaz:
http://arxiv.org/abs/2409.18457
Tabular reinforcement learning methods cannot operate directly on continuous state spaces. One solution for this problem is to partition the state space. A good partitioning enables generalization during learning and more efficient exploitation of pr
Externí odkaz:
http://arxiv.org/abs/2409.16791