Shared Autonomy Systems with Stochastic Operator Models
Authors: Clarissa Costen, Marc Rigter, Bruno Lacerda, Nick Hawes
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test SO-SAS on a simulated domain and a computer game, empirically showing it results in better performance compared to traditional formulations of shared autonomy systems. |
| Researcher Affiliation | Academia | Clarissa Costen , Marc Rigter , Bruno Lacerda and Nick Hawes Oxford Robotics Institute, University of Oxford, UK {clarissa, mrigter, bruno, nickh}@robots.ox.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions using data from the UAV domain from Feng et al., [2016] and Angry Birds, and collecting performance data, but does not provide concrete access information (link, DOI, repository, or explicit citation for dataset access) for the specific datasets used in their experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | To solve the SO-SAS MOMDP, we ran POMCP for 3 × 10^6 trials. |