Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 EMAIL |
| 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. |