Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
Authors: Sanjiban Choudhury, Shervin Javdani, Siddhartha Srinivasa, Sebastian Scherer
NeurIPS 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate BISECT on a collection of datasets spanning across a spectrum of synthetic problems and real-world planning applications. ... Table 1 shows the evaluation cost of all algorithms on various datasets normalized w.r.t BISECT. |
| Researcher Affiliation | Academia | Sanjiban Choudhury The Robotics Institute Carnegie Mellon University EMAIL Shervin Javdani The Robotics Institute Carnegie Mellon University EMAIL Siddhartha Srinivasa The Robotics Institute Carnegie Mellon University EMAIL Sebastian Scherer The Robotics Institute Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1: Decision Region Determination with Independent Bernoulli Test({Ri}m i=1 , θ, x T ) |
| Open Source Code | Yes | Open-source code and details can be found here: https://github.com/sanjibac/matlab_learning_collision_checking |
| Open Datasets | No | The paper mentions using 'synthetic problems', 'real-world planning applications', '7D arm planning dataset', and 'experimental data collected from a full scale helicopter' but does not provide specific links, DOIs, repositories, or formal citations for public access to these datasets. |
| Dataset Splits | No | The paper does not explicitly provide information on training, validation, or test dataset splits or cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU models, GPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper provides a link to a MATLAB repository (https://github.com/sanjibac/matlab_learning_collision_checking) which implies MATLAB is used, but it does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the general setup of experiments (e.g., evaluating on synthetic and real-world planning problems, comparing with various heuristics), but it does not provide specific hyperparameter values, optimizer settings, or detailed training configurations. |