Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Authors: Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, Joseph J. Lim
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi-modal policy. The video of our experiments is available at http://sites.google.com/view/nips17intentiongan. |
| Researcher Affiliation | Academia | Karol Hausman , Yevgen Chebotar , Stefan Schaal , Gaurav Sukhatme , Joseph J. Lim University of Southern California, Los Angeles, CA, USA Max-Planck-Institute for Intelligent Systems, Tübingen, Germany {hausman, ychebota, sschaal, gaurav, limjj}@usc.edu |
| Pseudocode | No | The paper describes the mathematical formulation of the proposed method but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any link to open-source code for the described methodology. It only links to a video of experiments. |
| Open Datasets | No | The paper describes that demonstrations are used to train the multi-modal policy and that expert policies were used to create the combined dataset, but it does not provide concrete access information (link, DOI, citation) to a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper mentions 'simulation results' and 'simulated robotics tasks' but does not specify any hardware details (e.g., CPU, GPU models) used for running the simulations or training the models. |
| Software Dependencies | No | The paper mentions various frameworks and techniques like 'neural networks', 'TRPO', 'GANs', but it does not provide specific software names with version numbers (e.g., Python 3.x, TensorFlow x.y). |
| Experiment Setup | Yes | During our experiments, we anneal the noise similar to [32], as the generator policy improves towards the end of the training. For the continuous latent variable, we show a span of different intentions between -1 and 1 in the 0.2 intervals. |