CoMic: Complementary Task Learning & Mimicry for Reusable Skills
Authors: Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We systematically compare a variety of different network architectures across different data regimes both in terms of imitation performance as well as transfer to challenging locomotion tasks. |
| Researcher Affiliation | Collaboration | 1Deep Mind, London 2Imperial College, London, work done during an internship at Deep Mind. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The acknowledgements mention 'Saran Tunyasuvunakool for infrastructure and opensourcing support' but there is no explicit statement or link confirming the release of the paper's source code. |
| Open Datasets | Yes | All motion capture data was obtained from the CMU mocap database. 1mocap.cs.cmu.edu |
| Dataset Splits | No | The paper describes using motion capture data for 'training' and then evaluating on 'transfer tasks' and 'out-of-sample tasks,' but it does not specify explicit training, validation, and test dataset splits with percentages or sample counts for the motion capture data used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like V-MPO, RHPO, and Mu Jo Co, but does not specify their version numbers or any other software dependencies with version information. |
| Experiment Setup | Yes | We regularize the latent embedding with a standard Gaussian prior by adding a KL loss term to the V-MPO losses: ... where the coefficient β controls the strength of the regularization. ... We trained MLP low-level controllers on the Locomotion clip set with latent space dimensions D = 20, 40 and 60 and a range of KL regularization strengths β. |