Associate Latent Encodings in Learning from Demonstrations
Authors: Hang Yin, Francisco Melo, Aude Billard, Ana Paiva
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 {fmelo, ana.paiva}@inesc-id.pt |
| 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. |