Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
PALMER: Perception - Action Loop with Memory for Long-Horizon Planning
Authors: Onur Beker, Mohammad Mohammadi, Amir Zamir
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,245 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,823 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments are performed in Vi ZDoom [39], Habitat [40], and the Maze2D benchmark [41]. |
| Researcher Affiliation | Academia | Onur Beker Mohammad Mohammadi Amir Zamir Swiss Federal Institute of Technology (EPFL) |
| Pseudocode | Yes | Algorithm 1 R-PRM (Roadmap Construction) and Algorithm 2 R-PRM (Trajectory Restitching Given the Constructed Roadmap) |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | Our experiments are performed in Vi ZDoom [39], Habitat [40], and the Maze2D benchmark [41]. |
| Dataset Splits | No | The paper mentions "offline training dataset" and "test apartments" but does not specify exact percentages or sample counts for train/validation/test splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory) to run its experiments in the main text. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers. |
| Experiment Setup | Yes | dp and c Q are hyperparameters. ... We randomly sample a transition (st, at, st+1) and a time difference T, and set the goal state as sg := st+T , as in hindsight relabelling. |