Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning
Authors: Fahad Islam, Oren Salzman, Maxim Likhachev
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 {fi,osalzman}@andrew.cmu.edu, maxim@cs.cmu.edu |
| 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. |