Learning Physical Intuition of Block Towers by Example
Authors: Adam Lerer, Sam Gross, Rob Fergus
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This data allows us to train large convolutional network models which can accurately predict the outcome, as well as estimating the block trajectories. ... Table 1 compares the accuracy for fall prediction of several deep networks and baselines described in Section 2.4. ... Fig. 6 compares Phys Net to 10 human subjects on the same set of synthetic and real test images. |
| Researcher Affiliation | Industry | Adam Lerer ALERER@FB.COM Facebook AI Research Sam Gross SGROSS@FB.COM Facebook AI Research Rob Fergus ROBFERGUS@FB.COM Facebook AI Research |
| Pseudocode | No | The paper describes network architectures and processes in prose and diagrams (e.g., Fig. 4), but does not contain pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | Yes | UETorch: We introduce an open-source combination of the Unreal game engine and the Torch deep learning environment, that is simple and efficient to use. ... The UETorch package can be downloaded freely at http: //github.com/facebook/UETorch. |
| Open Datasets | No | The paper describes generating a synthetic dataset: 'A simulation was developed in UETorch that generated vertical stacks of 2, 3, or 4 colored blocks in random configurations. ... A total of 180,000 simulations were performed...' However, it does not provide a direct link or citation for public access to this specific collected dataset. |
| Dataset Splits | Yes | A total of 180,000 simulations were performed, balanced across number of blocks and stable/unstable configurations. 12,288 examples were reserved for validation. |
| Hardware Specification | No | The paper does not specify the exact hardware components (e.g., specific CPU or GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Unreal Engine 4 (UE4)' and the 'Torch' framework, but it does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We replaced the final linear layer with a single logistic output and fine-tuned the entire network with SGD on the blocks dataset. Grid search was performed over learning rates. |