In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Task planning can require defining myriad domain knowledge about the world in which a
robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to …

Recent works have shown how the reasoning capabilities of Large Language Models
(LLMs) can be applied to domains beyond natural language processing, such as planning …

Long-horizon tasks, usually characterized by complex subtask dependencies, present a
significant challenge in manipulation planning. Skill chaining is a practical approach to …

Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Task planning can require defining myriad domain knowledge about the world in which a
robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to …

Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw
sensory inputs, but have yet to achieve the kind of broad generalization and applicability …

The history of learning for control has been an exciting back and forth between two broad
classes of algorithms: planning and reinforcement learning. Planning algorithms effectively …