Zobrazeno 1 - 10
of 301
pro vyhledávání: '"Sycara, Katia P"'
Autor:
Wang, Zirui, Zhao, Xinran, Stepputtis, Simon, Kim, Woojun, Wu, Tongshuang, Sycara, Katia, Xie, Yaqi
Understanding and predicting human actions has been a long-standing challenge and is a crucial measure of perception in robotics AI. While significant progress has been made in anticipating the future actions of individual agents, prior work has larg
Externí odkaz:
http://arxiv.org/abs/2411.01455
Autor:
Li, Bowen, Li, Zhaoyu, Du, Qiwei, Luo, Jinqi, Wang, Wenshan, Xie, Yaqi, Stepputtis, Simon, Wang, Chen, Sycara, Katia P., Ravikumar, Pradeep Kumar, Gray, Alexander G., Si, Xujie, Scherer, Sebastian
Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural networks. However, most of the existing benchmarks for NeSy AI fail to provide long-horizon reasoning tasks wit
Externí odkaz:
http://arxiv.org/abs/2411.00773
Autor:
Aryan, FNU, Stepputtis, Simon, Bhagat, Sarthak, Campbell, Joseph, Lee, Kwonjoon, Mahjoub, Hossein Nourkhiz, Sycara, Katia
Scene understanding is a fundamental capability needed in many domains, ranging from question-answering to robotics. Unlike recent end-to-end approaches that must explicitly learn varying compositions of the same scene, our method reasons over their
Externí odkaz:
http://arxiv.org/abs/2410.22626
Autor:
Lin, Muhan, Shi, Shuyang, Guo, Yue, Chalaki, Behdad, Tadiparthi, Vaishnav, Pari, Ehsan Moradi, Stepputtis, Simon, Campbell, Joseph, Sycara, Katia
The correct specification of reward models is a well-known challenge in reinforcement learning. Hand-crafted reward functions often lead to inefficient or suboptimal policies and may not be aligned with user values. Reinforcement learning from human
Externí odkaz:
http://arxiv.org/abs/2410.17389
Informative path planning (IPP) is an important planning paradigm for various real-world robotic applications such as environment monitoring. IPP involves planning a path that can learn an accurate belief of the quantity of interest, while adhering t
Externí odkaz:
http://arxiv.org/abs/2410.17186
Volume rendering in neural radiance fields is inherently time-consuming due to the large number of MLP calls on the points sampled per ray. Previous works would address this issue by introducing new neural networks or data structures. In this work, W
Externí odkaz:
http://arxiv.org/abs/2410.19831
Test-time adaptation, which enables models to generalize to diverse data with unlabeled test samples, holds significant value in real-world scenarios. Recently, researchers have applied this setting to advanced pre-trained vision-language models (VLM
Externí odkaz:
http://arxiv.org/abs/2410.12790
Autor:
Li, Huao, Mahjoub, Hossein Nourkhiz, Chalaki, Behdad, Tadiparthi, Vaishnav, Lee, Kwonjoon, Moradi-Pari, Ehsan, Lewis, Charles Michael, Sycara, Katia P
Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks. However, the learned language is usually not interpretable to humans
Externí odkaz:
http://arxiv.org/abs/2409.17348
Autor:
Gadipudi, Srikar Babu, Deolasee, Srujan, Kailas, Siva, Luo, Wenhao, Sycara, Katia, Kim, Woojun
Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective
Externí odkaz:
http://arxiv.org/abs/2409.16830
Autor:
Ho, Cherie, Kim, Seungchan, Moon, Brady, Parandekar, Aditya, Harutyunyan, Narek, Wang, Chen, Sycara, Katia, Best, Graeme, Scherer, Sebastian
Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on robots exploring structured indoor environments which are often predictable and composed of repeating patterns. Most existing a
Externí odkaz:
http://arxiv.org/abs/2409.15590