Learning VIsual Predictive Models of Physics for Playing Billiards
Authors: Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik
ICLR 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our agent can accurately plan actions for playing a simulated billiards game, which requires pushing a ball into a target position or into collision with another ball. |
| Researcher Affiliation | Academia | Katerina Fragkiadaki Pulkit Agrawal Sergey Levine Jitendra Malik Electrical Engineering and Computer Science University of California, Berkeley (katef,pulkitag,svlevine,malik)@berkeley.edu |
| Pseudocode | No | The paper describes processes in narrative text and uses diagrams to illustrate concepts (e.g., Figure 5 for generating visual imaginations), but it does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'We use the publicly available Caffe package for training our model.' (Section 3.1), referring to a third-party tool, but it does not provide an explicit statement or link for the open-sourcing of the authors' own implementation or experimental code. |
| Open Datasets | No | For model learning, we generate sequences of ball motions in a randomly sampled world configuration. ... For training, we pre-generated 10K such sequences. (The paper describes creating its own dataset but provides no information about its public availability or how to access it.) |
| Dataset Splits | No | The paper mentions training and testing on 'random worlds sampled from the same distribution as the training data' and 'worlds sampled from a different distribution', but it does not explicitly specify distinct training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, processor types, memory amounts, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper states 'We use the publicly available Caffe package for training our model.' (Section 3.1). While it names a software package, it does not specify its version number, nor does it list any other software dependencies with version details. |
| Experiment Setup | Yes | The model is trained by minimizing the Euclidean loss between ground-truth and predicted object velocities for h time steps in the future. ... We weigh the loss in a manner that errors in predictions at a shorter time horizon are penalized more than predictions at a longer time horizon. This weighting is achieved using penalty weights wk = exp( k 1 4 ). ... The length of each sequence was sampled from the range [20, 200]. The length of the walls was sampled from a range of [300 pixels, 550 pixels]. Balls were of radius 25 pixels and uniform density. Force direction was uniformly sampled and the force magnitude was sampled from the range [30K Newtons, 80K Newtons]. Forces were only applied on the first frame. The size of visual glimpses is 600x600 pixels. ... For training, we pre-generated 10K such sequences. We constructed minibatches by choosing 50 random subsets of 20 consequent frames from this pre-generated dataset. |