Bayesian Active Edge Evaluation on Expensive Graphs
Authors: Sanjiban Choudhury, Siddhartha Srinivasa, Sebastian Scherer
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our approach outperforms several state-of-the-art algorithms on a spectrum of planning problems for mobile robots, manipulators and autonomous helicopters. and We evaluate our approach on a spectrum of planning problems including a manipulator and a helicopter. and Table 1 shows the normalized evaluation cost of an algorithm |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, University of Washington, USA 2 The Robotics Institute, Carnegie Mellon University, USA |
| Pseudocode | Yes | To aid in implementation, we provide a pseudo-code for DIRECT in Alg. 1, 2 and 3. and Algorithm 1: DIRECT (Hact, R, X, c), Algorithm 2: DRD (H , R), Algorithm 3: Weight EC (H , R, i), Algorithm 4: Create Decision Tree (Γ, Hact, R, X, c) |
| Open Source Code | Yes | Opensource code and details can be found here: https://github.com/ sanjibac/matlab learning collision checking |
| Open Datasets | Yes | A dataset of n worlds is sampled from a designed generative model. Each edge is evaluated on each world to create a test outcome matrix X Rn |T |. A library of paths is created along with a binary membership matrix R Rn m encoding the validity of a path on a world. 10% of the data is used for test, remainder for training. Typical values used are n : 1000, m : 500. Details about the dataset generation are described in [Choudhury et al., 2017b]. Opensource code and details can be found here: https://github.com/ sanjibac/matlab learning collision checking |
| Dataset Splits | No | The paper states '10% of the data is used for test, remainder for training' but does not mention a separate validation set or split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or processing units) used for running the experiments. |
| Software Dependencies | No | The paper mentions that the code is in a 'matlab learning collision checking' repository, implying MATLAB is used, but no specific version numbers for MATLAB or any other software dependencies are provided. |
| Experiment Setup | No | The paper mentions parameters like the threshold η and the bias term α (for BISECT) but does not provide specific values used for these or any other hyperparameters or training configurations for the experimental setup. |