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..
Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning
Authors: Fahad Islam, Oren Salzman, Maxim Likhachev
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. |
| Researcher Affiliation | Academia | Fahad Islam, Oren Salzman and Maxim Likhachev The Robotics Institute, Carnegie Mellon University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 User-guided Planning (A); Algorithm 2 User-guided MHA* |
| Open Source Code | No | The paper does not mention or provide a link to any open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using a 34-DOF WAREC humanoid robot and motion primitives, which suggests internal data or simulation, but does not provide any public access information (link, DOI, citation) for a dataset. |
| Dataset Splits | No | The paper mentions running experiments over "ten different trials" but does not specify any training, validation, or test dataset splits or ratios. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU, GPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions MHA* as the planning algorithm but does not provide specific software versions or dependencies (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | The parameter values used in both experiments are ω1(200), ω2(50) and ε(50) for the heuristic-based, and τ(30) and ω(10) for the vacillation-based stagnation-region detection. |