Towards Uniformly Superhuman Autonomy via Subdominance Minimization
Authors: Brian Ziebart, Sanjiban Choudhury, Xinyan Yan, Paul Vernaza
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our approach on a computer cursor pointing task, producing behavior that is 78% superhuman, while minimizing demonstration suboptimality provides 50% superhuman behavior and only 72% even after selective data cleaning. |
| Researcher Affiliation | Collaboration | 1Computer Science, University of Illinois Chicago 2Aurora Innovation. Correspondence to: B. Ziebart <bziebart@uic.edu>. |
| Pseudocode | Yes | Algorithm 1 Update w and α from demonstration(s) ξ |
| Open Source Code | No | No statement regarding the availability of open-source code for the methodology is provided in the paper. |
| Open Datasets | No | We focus our experiments on human-generated demonstrations. We analyze pointing task data gathered from 20 non-motor impaired individuals each performing 300 pointing tasks. |
| Dataset Splits | No | We randomly split the dataset into a training set of 200 tasks and a testing set of 100 tasks. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | We optimize each αk using stochastic exponentiated gradient descent: αk αkeηt(fk( ξ) fk(ξ ) λαk) using an appropriately decaying learning rate ηt, as shown in Algorithm 1. |