Unlocking underrepresented use-cases for large language model-driven human-robot task planning
Author:
Affiliation:
1. Department of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA
2. Institute of Technology, University of Tartu, Tartu, Estonia
Funder
European Social Fund
European Regional Development Fund
Ministry of Economic Affairs and Communications in Estonia
European Union
NSF/Air Force
Los Alamos National Laboratory
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/01691864.2024.2366974
Reference57 articles.
1. Bommasani R Hudson DA Adeli E. On the opportunities and risks of foundation models; 2021. Available from: https://arxiv.org/abs/2108.07258
2. Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration
3. ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
4. Huang W Abbeel P Pathak D et al. Language models as zero-shot planners: extracting actionable knowledge for embodied agents; 2022. Available from: https://arxiv.org/abs/2201.07207
5. Huang W Xia F Xiao T et al. Innermonologue: embodied reasoning through planning with language models; 2022. CoRL 2022 (to appear). Available from: https://innermonologue.github.io/
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