Llm for Reward Function

[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....

[TOC] Title: Eureka Human Level Reward Design via Coding Large Language Models 2023 Author: Yecheng Jason Ma et. al. Publish Year: 19 Oct 2023 Review Date: Fri, Oct 27, 2023 url: https://arxiv.org/pdf/2310.12931.pdf Summary of paper Motivation harnessing LLMs to learn complex low-level manipulation tasks, remains an open problem. we bridge this fundamental gap by using LLMs to produce rewards that can be used to acquire conplex skill via reinforcement learning. Contribution Eureka generate reward functions that outperform expert human-engineered rewards....