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..
Learning from Demonstration with Weakly Supervised Disentanglement
Authors: Yordan Hristov, Subramanian Ramamoorthy
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach is evaluated in the context of two table-top robot manipulation tasks performed by a PR2 robot that of dabbing liquids with a sponge (forcefully pressing a sponge and moving it along a surface) and pouring between different containers. and 5 EXPERIMENTS and 6 RESULTS & DISCUSSION |
| Researcher Affiliation | Academia | Yordan Hristov School of Informatics University of Edinburgh EMAIL Subramanian Ramamoorthy School of Informatics University of Edinburgh EMAIL |
| Pseudocode | Yes | Algorithms 1 and 2 provide pseudo-code for the trajectory generation procedures described in Section 3. |
| Open Source Code | No | We have made videos of the tasks and data available see supplementary materials at: https://sites.google.com/view/weak-label-lfd. The paper mentions data availability but does not explicitly state that the source code for the methodology is released. |
| Open Datasets | Yes | We release a dataset of subjective concepts grounded in multi-modal demonstrations. and We have made videos of the tasks and data available see supplementary materials at: https://sites.google.com/view/weak-label-lfd. |
| Dataset Splits | Yes | The size of the total dataset after augmentation is 1000 demonstrations which are split according to a 90-10 training-validation split. |
| Hardware Specification | No | The paper mentions using a 'PR2 robot' and 'Kinect2' for data capture, but does not provide specific hardware details (e.g., GPU/CPU models, memory) used for model training or inference. |
| Software Dependencies | No | The models are implemented in Py Torch (Adam et al., 2017) and optimised using the Adam optimiser (Kingma & Ba, 2014). The paper names PyTorch but does not specify its version number. |
| Experiment Setup | Yes | Across all experiments, training is performed for a fixed number of 100 epochs using a batch size of 8. The dimensionality of the latent space |c| = 8 across all experiments. The Adam optimizer (Kingma & Ba, 2014) is used through the learning process with the following values for its parameters (learningrate = 0.001, β1 = 0.9, β2 = 0.999, eps = 1e 08, weightdecayrate = 0, amsgrad = False). For all experiments, the values (unless when set to 0) for the three coefficients from Equation 9 are: α = 1, β = 0.1, γ = 10. |