Active representation learning for general task space with applications in robotics
Authors: Yifang Chen, Yingbing Huang, Simon S. Du, Kevin G. Jamieson, Guanya Shi
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate different instantiations of our meta algorithm in synthetic datasets and robotics problems, from pendulum simulations to real-world drone flight datasets. On average, our algorithms outperform baselines by 20% 70%. |
| Researcher Affiliation | Academia | 1 Paul G. Allen School of Computer Science & Engineering University of Washington, Seattle,WA {yifangc, ssdu, jamieson, guanyas}@cs.washington.edu 2 University of Illinois Urbana-Champaign, Champaign, IL {yh21}@illinois.edu 3 Robotics Institute, Carnegie Mellon University, Pittsburgh, PA {{guanyas }@andrew.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Active multi-task representation learning (general templates) |
| Open Source Code | Yes | 1Code in https://github.com/cloudways X/ALMulti Task_Robotics |
| Open Datasets | Yes | Real-world drone flight dataset [7]. The Neural-Fly dataset [7] includes real flight trajectories using two different drones in various wind conditions. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages or exact counts) for any of the datasets used. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, or memory specifications) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions using a 'gradient-descent joint training oracle' but does not specify any particular software packages or their version numbers required for reproducibility. |
| Experiment Setup | No | The paper mentions using a 'gradient-descent joint training oracle' but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations in the main text. |