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 | Conference PDF | Archive PDF | Plain Text | 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 lsw@brown.edu Thanard Kurutach, Tom as Lozano-P erez, MIT Computer Science and Artificial Intelligence Laboratory Cambridge, MA 02139, USA {kurutach, tlp, lpk}@csail.mit.edu 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. |