Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Exploratory Retrieval-Augmented Planning For Continual Embodied Instruction Following
Authors: Minjong Yoo, Jinwoo Jang, Wei-Jin Park, Honguk Woo
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments with Virtual Home, ALFRED, and CARLA, our approach demonstrates robustness against a variety of embodied instruction following scenarios involving different instruction scales and types, and non-stationarity degrees, and it consistently outperforms other state-of-the-art LLM-based task planning approaches in terms of both goal success rate and execution efficiency. |
| Researcher Affiliation | Collaboration | Minjong Yoo1, Jinwoo Jang1, Wei-jin Park2, Honguk Woo1 1Department of Computer Science and Engineering, Sungkyunkwan University 2Acryl Inc. |
| Pseudocode | Yes | Algorithm 1 Detailed implementation of Ex RAP framework |
| Open Source Code | Yes | We provide implementation details in Appendix, and also release source code. |
| Open Datasets | Yes | Through experiments with Virtual Home [8], ALFRED [9] and CARLA [10], we demonstrate that the Ex RAP framework achieves competitive performance in both task success and efficiency compared to several state-of-the-art embodied planning methods, including ZSP [11], Say Can [1], Prog Prompt [3], and LLM-Planner [12]. |
| Dataset Splits | No | The paper uses 100 trajectories across 10 different environment settings in Virtual Home, and 50 trajectories in ALFRED and CARLA for in-context learning and evaluation, but does not specify explicit train/validation/test splits by percentage or sample count. |
| Hardware Specification | Yes | Our framework is implemented using Python v3.10 and trained on a system of an Intel(R) Core (TM) i9-10980XE processor and two NVIDIA RTX A6000 GPUs. |
| Software Dependencies | Yes | Our framework is implemented using Python v3.10 and trained on a system of an Intel(R) Core (TM) i9-10980XE processor and two NVIDIA RTX A6000 GPUs. |
| Experiment Setup | Yes | The hyperparameter settings for baselines are summarized in Table A.3. The hyperparameter settings for Ex RAP are summarized in Table A.4. (e.g., LLM Llama-3-8B (Default), Temperature 0.33, Filtering threshold θ in (8) 0.5, Weights for exploration value w R in (12) 1.0, Weights for exploitation value w T in (12) 0.01) |