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[TOC] Title: Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning Author: Thomas Carta el. al. Publish Year: 6 Sep 2023 Review Date: Tue, Apr 23, 2024 url: arXiv:2302.02662v3 Summary of paper Summary The author considered an agent using an LLM as a policy that is progressively updated as the agent interacts with the environment, leveraging online reinforcement learning to improve its performance to solve goals (under the RL paradigm environment (MDP))...

[TOC] Title: “On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS).” Author: Pallagani, Vishal, et al. Publish Year: arXiv preprint arXiv:2401.02500 (2024). Review Date: Mon, Jan 29, 2024 url: Summary of paper Contribution The paper provides a comprehensive review of 126 papers focusing on the integration of Large Language Models (LLMs) within Automated Planning and Scheduling, a growing area in Artificial Intelligence (AI). It identifies eight categories where LLMs are applied in addressing various aspects of planning problems:...

[TOC] Title: Understanding Natural Language in Context Author: Avichai Levy et. al. Publish Year: ICAPS 2023 Review Date: Mon, Jan 29, 2024 url: https://ojs.aaai.org/index.php/ICAPS/article/view/27248 Summary of paper Contribution The paper discusses the increasing prevalence of applications with natural language interfaces, such as chatbots and personal assistants like Alexa, Google Assistant, Siri, and Cortana. While current dialogue systems mainly involve static robots, the challenge intensifies with cognitive robots capable of movement and object manipulation in home environments....

[TOC] Title: RePlan: Robotic Replanning with Perception and Language Models Author: Marta Skreta et. al. Publish Year: 8 Jan 2024 Review Date: Thu, Jan 25, 2024 url: arXiv:2401.04157v1 Summary of paper Motivation However, the challenge remains that even with syntac- tically correct plans, robots can still fail to achieve their intended goals. This failure can be attributed to imperfect plans proposed by LLMs or to unforeseeable environmental circumstances that hinder the execution of planned subtasks due to erroneous assumptions about the state of objects....