Zobrazeno 1 - 10
of 669
pro vyhledávání: '"Sycara, Katia"'
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
Autor:
Ho, Cherie, Zou, Jiaye, Alama, Omar, Kumar, Sai Mitheran Jagadesh, Chiang, Benjamin, Gupta, Taneesh, Wang, Chen, Keetha, Nikhil, Sycara, Katia, Scherer, Sebastian
Top-down Bird's Eye View (BEV) maps are a popular representation for ground robot navigation due to their richness and flexibility for downstream tasks. While recent methods have shown promise for predicting BEV maps from First-Person View (FPV) imag
Externí odkaz:
http://arxiv.org/abs/2407.08726
Publikováno v:
2024 IEEE International Conference on Robotics and Automation (ICRA) 2024
This paper introduces a novel transfer learning framework for deep multi-agent reinforcement learning. The approach automatically combines goal-conditioned policies with temporal contrastive learning to discover meaningful sub-goals. The approach inv
Externí odkaz:
http://arxiv.org/abs/2406.01377
Autor:
Wan, Zifu, Zhang, Pingping, Wang, Yuhao, Yong, Silong, Stepputtis, Simon, Sycara, Katia, Xie, Yaqi
Multi-modal semantic segmentation significantly enhances AI agents' perception and scene understanding, especially under adverse conditions like low-light or overexposed environments. Leveraging additional modalities (X-modality) like thermal and dep
Externí odkaz:
http://arxiv.org/abs/2404.04256
Autor:
Li, Samuel, Bhagat, Sarthak, Campbell, Joseph, Xie, Yaqi, Kim, Woojun, Sycara, Katia, Stepputtis, Simon
Task-oriented grasping of unfamiliar objects is a necessary skill for robots in dynamic in-home environments. Inspired by the human capability to grasp such objects through intuition about their shape and structure, we present a novel zero-shot task-
Externí odkaz:
http://arxiv.org/abs/2403.18062
This paper describes CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains. Unlike traditional neural network models, which either generate an output for each provided input, or an o
Externí odkaz:
http://arxiv.org/abs/2403.15974
Recently, large-scale pre-trained Vision-Language Models (VLMs) have demonstrated great potential in learning open-world visual representations, and exhibit remarkable performance across a wide range of downstream tasks through efficient fine-tuning.
Externí odkaz:
http://arxiv.org/abs/2403.12964