Learning Visual Servoing with Deep Features and Fitted Q-Iteration
Authors: Alex X. Lee, Sergey Levine, Pieter Abbeel
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the model for visual servoing in a simulated environment. |
| Researcher Affiliation | Collaboration | UC Berkeley, Department of Electrical Engineering and Computer Sciences Open AI International Computer Science Institute {alexlee gk,svlevine,pabbeel}@cs.berkeley.edu |
| Pseudocode | Yes | Algorithm 1 FQI with initialization of policy-independent parameters |
| Open Source Code | Yes | The environment for the synthetic car following benchmark is available online as the package City Sim3D1, and the code to reproduce our method and experiments is also available online2. |
| Open Datasets | Yes | Our choice of semantic features are derived from the VGG-16 network (Simonyan & Zisserman, 2014), which is a convolutional neural network trained for large-scale image recognition on the Image Net dataset (Deng et al., 2009). |
| Dataset Splits | Yes | We ran the overall algorithm for only S = 2 sampling iterations and chose the parameters that achieved the best performance on 10 validation trajectories. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. It only mentions a simulated environment. |
| Software Dependencies | No | The paper mentions software like Caffe and ADAM, but does not provide specific version numbers for the key software dependencies required for reproducibility. |
| Experiment Setup | Yes | The dynamics models were trained with ADAM using 10000 iterations, a batch size of 32, a learning rate of 0.001, and momentums of 0.9 and 0.999, and a weight decay of 0.0005. |