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..
Object-Based World Modeling in Semi-Static Environments with Dependent Dirichlet Process Mixtures
Authors: Lawson L. S. Wong, Thanard Kurutach, Tomás Lozano-Pérez, Leslie Pack Kaelbling
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments Approximate MAP inference for world modeling via ICM, MCMCDA, and the two-stage ICM-MCMC were tested on a simulated domain, and on a sequence of robot vision data constructed from the static scenes in Wong et al. [2015]. |
| Researcher Affiliation | Academia | Lawson L. S. Wong Brown University Providence, RI 02912 EMAIL Thanard Kurutach, Tom as Lozano-P erez, MIT Computer Science and Artificial Intelligence Laboratory Cambridge, MA 02139, USA EMAIL Leslie Pack Kaelbling |
| Pseudocode | Yes | Figure 2: ICM-MCMC, a two-stage inference algorithm for DDPMM, using ICM and MCMCDA [Oh et al., 2009]. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories, nor does it state that the code is being released or is available in supplementary materials. |
| Open Datasets | Yes | We used a simulated domain similar to the one given in the MCMCDA paper [Oh et al., 2009]. We also applied the same algorithms to the static robot vision data from Wong et al. [2015]. |
| Dataset Splits | No | The paper mentions using a simulated domain over '10 epochs' and '5 views per epoch', and concatenating 'static robot vision data from Wong et al. [2015]' to create dynamic scenes. However, it does not explicitly provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or predefined split citations) needed for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Noise parameters were similar to Oh et al. [2009]. between epochs, we assume that the location changes with isotropic Gaussian noise, standard deviation 0.1. Since changes were significant between epochs, we assumed a relatively low 0.5 probability of survival. To perform MAP inference on MCMCDA and ICM-MCMC, the most-likely sample was chosen, from 105 samples in MCMCDA, and 104 in the second stage of ICM-MCMC. |