Zobrazeno 1 - 10
of 18 550
pro vyhledávání: '"Pavone A."'
Transformers are a widespread and successful model architecture, particularly in Natural Language Processing (NLP) and Computer Vision (CV). The essential innovation of this architecture is the Attention Mechanism, which solves the problem of extract
Externí odkaz:
http://arxiv.org/abs/2410.13732
Autor:
Cho, Minkyoung, Cao, Yulong, Sun, Jiachen, Zhang, Qingzhao, Pavone, Marco, Park, Jeong Joon, Yang, Heng, Mao, Z. Morley
An important paradigm in 3D object detection is the use of multiple modalities to enhance accuracy in both normal and challenging conditions, particularly for long-tail scenarios. To address this, recent studies have explored two directions of adapti
Externí odkaz:
http://arxiv.org/abs/2410.12592
Autor:
Deglurkar, Sampada, Shen, Haotian, Muthali, Anish, Pavone, Marco, Margineantu, Dragos, Karkus, Peter, Ivanovic, Boris, Tomlin, Claire J.
We present a novel perspective on the design, use, and role of uncertainty measures for learned modules in an autonomous system. While in the current literature uncertainty measures are produced for standalone modules without considering the broader
Externí odkaz:
http://arxiv.org/abs/2410.12019
Autor:
Celestini, Davide, Afsharrad, Amirhossein, Gammelli, Daniele, Guffanti, Tommaso, Zardini, Gioele, Lall, Sanjay, Capello, Elisa, D'Amico, Simone, Pavone, Marco
Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for solving the trajectory generation problem, effectively combining the ben
Externí odkaz:
http://arxiv.org/abs/2410.11723
Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, prior work has mostly explored naive solutions such as fine-tuning, focus
Externí odkaz:
http://arxiv.org/abs/2410.09681
Hierarchical policies enable strong performance in many sequential decision-making problems, such as those with high-dimensional action spaces, those requiring long-horizon planning, and settings with sparse rewards. However, learning hierarchical po
Externí odkaz:
http://arxiv.org/abs/2410.07933
Future multi-spacecraft missions require robust autonomous trajectory optimization capabilities to ensure safe and efficient rendezvous operations. This capability hinges on solving non-convex optimal control problems in real time, although tradition
Externí odkaz:
http://arxiv.org/abs/2410.05585
Autor:
Huang, Zhiyu, Weng, Xinshuo, Igl, Maximilian, Chen, Yuxiao, Cao, Yulong, Ivanovic, Boris, Pavone, Marco, Lv, Chen
Autonomous driving necessitates the ability to reason about future interactions between traffic agents and to make informed evaluations for planning. This paper introduces the \textit{Gen-Drive} framework, which shifts from the traditional prediction
Externí odkaz:
http://arxiv.org/abs/2410.05582
Autor:
Agia, Christopher, Sinha, Rohan, Yang, Jingyun, Cao, Zi-ang, Antonova, Rika, Pavone, Marco, Bohg, Jeannette
Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies at test time and provide early warnings of failure are necessary to fac
Externí odkaz:
http://arxiv.org/abs/2410.04640
Autor:
Zhang, Di, Wu, Jianbo, Lei, Jingdi, Che, Tong, Li, Jiatong, Xie, Tong, Huang, Xiaoshui, Zhang, Shufei, Pavone, Marco, Li, Yuqiang, Ouyang, Wanli, Zhou, Dongzhan
This paper presents an advanced mathematical problem-solving framework, LLaMA-Berry, for enhancing the mathematical reasoning ability of Large Language Models (LLMs). The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to
Externí odkaz:
http://arxiv.org/abs/2410.02884