[ICRA-24] CAPE: Corrective Actions from Precondition Errors using Large Language Models

Related Links:

• Webpage with Supplementary Materials
• Paper:
  • arXiv (best version)
  • Previous version (FMDM @ NeurIPS 2022)

NOTES:

• In this paper, we introduce CAPE: an approach to address errors encountered by a robot or agent when executing a plan.
  • We use large language models (LLM) to perform planning and, more importantly, infer the causes of errors.
  • We assume that these errors lie in skill preconditions that are not being met by the agent or its environment.
  • This work builds upon a submission made to the NeurIPS 2022 Workshop on Foundation Models for Decision Making (FDM) .
• We showcase our method in simulation (AI2THOR) and with a real robot (Boston Dynamics Spot quadruped robot).

Citation:

Latest Version (ICRA 2024)

S. Sundara Raman, V. Cohen, I. Idrees, E. Rosen, R. Mooney, S. Tellex, and D. Paulius (2024). “CAPE: Corrective Actions from Precondition Errors using Large Language Models”. In: 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE.

Workshop Version (NeurIPS 2022 FDM Workshop)

S. Sundara Raman, V. Cohen, E. Rosen, I. Idrees, D. Paulius, and S. Tellex (2022). “Planning With Large Language Models Via Corrective Re-Prompting”. In: NeurIPS Workshop on Foundation Models for Decision Making (FMDM), NeurIPS 2022.