Zobrazeno 1 - 10
of 18 569
pro vyhledávání: '"Pavone, A"'
Sequentially solving similar optimization problems under strict runtime constraints is essential for many applications, such as robot control, autonomous driving, and portfolio management. The performance of local optimization methods in these settin
Externí odkaz:
http://arxiv.org/abs/2411.02158
Autor:
Celestini, Davide, Gammelli, Daniele, Guffanti, Tommaso, D'Amico, Simone, Capello, Elisa, Pavone, Marco
Publikováno v:
IEEE Robotics and Automation Letters, vol. 9, n. 11, pp. 9820-9827, Nov. 2024
Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. However, for most problems of interest, MPC relies on the recursive sol
Externí odkaz:
http://arxiv.org/abs/2410.23916
Autor:
Fan, Zhiwen, Zhang, Jian, Cong, Wenyan, Wang, Peihao, Li, Renjie, Wen, Kairun, Zhou, Shijie, Kadambi, Achuta, Wang, Zhangyang, Xu, Danfei, Ivanovic, Boris, Pavone, Marco, Wang, Yue
Reconstructing and understanding 3D structures from a limited number of images is a well-established problem in computer vision. Traditional methods usually break this task into multiple subtasks, each requiring complex transformations between differ
Externí odkaz:
http://arxiv.org/abs/2410.18956
Publikováno v:
Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR2024
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