Data Quality in Imitation Learning
Authors: Suneel Belkhale, Yuchen Cui, Dorsa Sadigh
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the combined effect of these two key properties in imitation learning theoretically, and we empirically analyze models trained on a variety of different data sources. |
| Researcher Affiliation | Academia | Suneel Belkhale Stanford University belkhale@stanford.edu Yuchen Cui Stanford University yuchenc@stanford.edu Dorsa Sadigh Stanford University dorsa@stanford.edu |
| Pseudocode | No | No pseudocode or algorithm blocks explicitly labeled as such were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | In Table 1, we consider single and multi-human datasets from the Square and Can tasks from robomimic [37]. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., percentages, sample counts, or references to predefined splits) needed for reproduction. It mentions 'training' and 'test time' in the context of distribution shift and high/low data regimes, but not specific splits. |
| Hardware Specification | No | The paper does not specify the hardware used for running experiments, such as GPU or CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions that 'BC uses an MLP architecture' and 'Transformer architecture results' but does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | We train Behavior Cloning (BC) with data generated with system noise and policy noise in two environments: PMObstacle... and Square... BC uses an MLP architecture. (Section 5.1). Also, the tables show varied noise levels (e.g., "σs = 0.01", "σp = 0.01") and episode counts ("1000 episodes", "10 episodes"). |