Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
Authors: Sanjiban Choudhury, Shervin Javdani, Siddhartha Srinivasa, Sebastian Scherer
NeurIPS 2017 | Conference 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 sanjiban@cmu.edu Shervin Javdani The Robotics Institute Carnegie Mellon University sjavdani@cmu.edu Siddhartha Srinivasa The Robotics Institute Carnegie Mellon University siddh@cs.cmu.edu Sebastian Scherer The Robotics Institute Carnegie Mellon University basti@cs.cmu.edu |
| 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. |