CoPAL: Corrective Planning of Robot Actions with Large Language Models

• URL: http://arxiv.org/abs/2310.07263v1
• Date: Wed, 11 Oct 2023 07:39:42 GMT
• Title: CoPAL: Corrective Planning of Robot Actions with Large Language Models
• Authors: Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Stephan Hasler, Daniel Tanneberg, Michael Gienger
• Abstract summary: We propose a system architecture that orchestrates a seamless interplay between cognitive levels, encompassing reasoning, planning, and motion generation. At its core lies a novel replanning strategy that handles physically grounded, logical, and semantic errors in the generated plans.
• Score: 8.209152055117283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge. Addressing this imperative, this study contributes to the field of Large Language Models (LLMs) applied to task and motion planning for robots. We propose a system architecture that orchestrates a seamless interplay between multiple cognitive levels, encompassing reasoning, planning, and motion generation. At its core lies a novel replanning strategy that handles physically grounded, logical, and semantic errors in the generated plans. We demonstrate the efficacy of the proposed feedback architecture, particularly its impact on executability, correctness, and time complexity via empirical evaluation in the context of a simulation and two intricate real-world scenarios: blocks world, barman and pizza preparation.

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