Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Campbell, Joseph P."'
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:
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
Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches. A common approach enabling the ability to reason over visual data is Scene Graph Generation (S
Externí odkaz:
http://arxiv.org/abs/2403.12033
Autor:
Zabounidis, Renos, Oguntola, Ini, Zhao, Konghao, Campbell, Joseph, Stepputtis, Simon, Sycara, Katia
Concept bottleneck models (CBMs) are interpretable models that first predict a set of semantically meaningful features, i.e., concepts, from observations that are subsequently used to condition a downstream task. However, the model's performance stro
Externí odkaz:
http://arxiv.org/abs/2312.00192
Autor:
Stepputtis, Simon, Campbell, Joseph, Xie, Yaqi, Qi, Zhengyang, Zhang, Wenxin Sharon, Wang, Ruiyi, Rangreji, Sanketh, Lewis, Michael, Sycara, Katia
Deception and persuasion play a critical role in long-horizon dialogues between multiple parties, especially when the interests, goals, and motivations of the participants are not aligned. Such complex tasks pose challenges for current Large Language
Externí odkaz:
http://arxiv.org/abs/2311.05720
Autor:
Li, Huao, Chong, Yu Quan, Stepputtis, Simon, Campbell, Joseph, Hughes, Dana, Lewis, Michael, Sycara, Katia
Publikováno v:
in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Page 180-192, ACL
While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored. This study evaluates LLM-based agents in a multi-agent cooperati
Externí odkaz:
http://arxiv.org/abs/2310.10701
Intelligent agents such as robots are increasingly deployed in real-world, safety-critical settings. It is vital that these agents are able to explain the reasoning behind their decisions to human counterparts, however, their behavior is often produc
Externí odkaz:
http://arxiv.org/abs/2309.10346
This work focuses on anticipating long-term human actions, particularly using short video segments, which can speed up editing workflows through improved suggestions while fostering creativity by suggesting narratives. To this end, we imbue a transfo
Externí odkaz:
http://arxiv.org/abs/2309.05943
This paper introduces a novel state estimation framework for robots using differentiable ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble Kalman filter that employs stochastic neural networks to model the process
Externí odkaz:
http://arxiv.org/abs/2308.09870
The ability to model the mental states of others is crucial to human social intelligence, and can offer similar benefits to artificial agents with respect to the social dynamics induced in multi-agent settings. We present a method of grounding semant
Externí odkaz:
http://arxiv.org/abs/2307.01158