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..
Associate Latent Encodings in Learning from Demonstrations
Authors: Hang Yin, Francisco Melo, Aude Billard, Ana Paiva
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The implementation and results are demonstrated in a robotic handwriting scenario, where the visual sensory input and the arm joint writing motion are learned and coupled. We show the latent representations successfully construct a task manifold for the observed sensor modalities. Moreover, the learned associations can be exploited to directly synthesize arm joint handwriting motion from an image input in an end-to-end manner. |
| Researcher Affiliation | Academia | 1GAIPS, INESC-ID and Instituto Superior T ecnico, Universidade de Lisboa 2Learning Algorithms and Systems Laboratory, Ecole Polytechnique F ed erale de Lausanne {hang.yin, aude.billard}@epfl.ch EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | The code implementation and trained models are publicly accessible1. 1https://github.com/navigator8972/vae assoc |
| Open Datasets | No | The dataset used in the experiment is UJI Char Pen 2 dataset, from which, for simplicity, only one-stroke-formed alphabetical letters and digits are involved. No direct link, DOI, or formal citation for public access is provided within the paper text. |
| Dataset Splits | No | The other hyper parameters, including the length of the latent variable and the weight of association term, are selected according to the cross-validation of the reconstruction performance. However, specific percentages or counts for training, validation, or test splits are not provided. |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models, memory, or processing units) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions neural network models and ADAM for optimization but does not provide specific software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | The entire model is trained through the stochastic gradient descent with an adaptive moment estimation (ADAM) (Kingma and Ba 2015), a learning rate of 1e 4 and a batch size of 64. The other hyper parameters, including the length of the latent variable and the weight of association term, are selected according to the cross-validation of the reconstruction performance. |