Neural Graph Evolution: Towards Efficient Automatic Robot Design
Authors: Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As shown in experiments, NGE is the first algorithm that can automatically discover kinematically preferred robotic graph structures, such as a fish with two symmetric flat side-fins and a tail, or a cheetah with athletic front and back legs. In this section, we demonstrate the effectiveness of NGE on various evolution tasks. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science, University of Toronto 2 Vector Institute 3 NVIDIA |
| Pseudocode | Yes | Algorithm 1 Neural Graph Evolution |
| Open Source Code | No | The paper does not provide a statement or link to open-source code for the methodology described. |
| Open Datasets | No | Our experiments are simulated with Mu Jo Co. We design the following environments to test the algorithms. Fish Env: In the fish environment, graph consists of ellipsoids. The reward is the swimming-speed along the y-direction. We denote the reference human-engineered graph (Tassa et al., 2018) as GF . Walker Env: We also define a 2D environment walker constructed by cylinders, where the goal is to move along x-direction as fast as possible. No concrete access information for these custom environments. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. It mentions training RL agents and evaluating fitness, but no explicit data partitioning information. |
| Hardware Specification | Yes | NGE is extremely efficient, it finds plausible robotic structures within a day on a single 64 CPU-core Amazon EC2 machine. A NGE session with 16-core m5.4xlarge ($0.768 per Hr) AWS machine can achieve almost the same performance with 64-core m4.16xlarge ($3.20 per Hr) in Fish environment in the same wall-clock time. |
| Software Dependencies | No | The paper mentions the use of 'Mu Jo Co' as a simulator but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We do a grid search on the hyper-parameters as summarized in Appendix E, and show the averaged curve of each method. Table 2: Hyperparameter grid search options. Table 3: Hyperparameters grid search options for NGE. |