CAPE: Corrective Actions from Precondition Errors using Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866910359470735360 |
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| author | Raman, Shreyas Sundara Cohen, Vanya Idrees, Ifrah Rosen, Eric Mooney, Ray Tellex, Stefanie Paulius, David |
| author_facet | Raman, Shreyas Sundara Cohen, Vanya Idrees, Ifrah Rosen, Eric Mooney, Ray Tellex, Stefanie Paulius, David |
| contents | Extracting commonsense knowledge from a large language model (LLM) offers a path to designing intelligent robots. Existing approaches that leverage LLMs for planning are unable to recover when an action fails and often resort to retrying failed actions, without resolving the error's underlying cause. We propose a novel approach (CAPE) that attempts to propose corrective actions to resolve precondition errors during planning. CAPE improves the quality of generated plans by leveraging few-shot reasoning from action preconditions. Our approach enables embodied agents to execute more tasks than baseline methods while ensuring semantic correctness and minimizing re-prompting. In VirtualHome, CAPE generates executable plans while improving a human-annotated plan correctness metric from 28.89% to 49.63% over SayCan. Our improvements transfer to a Boston Dynamics Spot robot initialized with a set of skills (specified in language) and associated preconditions, where CAPE improves the correctness metric of the executed task plans by 76.49% compared to SayCan. Our approach enables the robot to follow natural language commands and robustly recover from failures, which baseline approaches largely cannot resolve or address inefficiently. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_09935 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | CAPE: Corrective Actions from Precondition Errors using Large Language Models Raman, Shreyas Sundara Cohen, Vanya Idrees, Ifrah Rosen, Eric Mooney, Ray Tellex, Stefanie Paulius, David Artificial Intelligence Computation and Language Machine Learning Robotics 68T20, 68T50 I.2.7; I.2.8; I.2.2; I.2.4 Extracting commonsense knowledge from a large language model (LLM) offers a path to designing intelligent robots. Existing approaches that leverage LLMs for planning are unable to recover when an action fails and often resort to retrying failed actions, without resolving the error's underlying cause. We propose a novel approach (CAPE) that attempts to propose corrective actions to resolve precondition errors during planning. CAPE improves the quality of generated plans by leveraging few-shot reasoning from action preconditions. Our approach enables embodied agents to execute more tasks than baseline methods while ensuring semantic correctness and minimizing re-prompting. In VirtualHome, CAPE generates executable plans while improving a human-annotated plan correctness metric from 28.89% to 49.63% over SayCan. Our improvements transfer to a Boston Dynamics Spot robot initialized with a set of skills (specified in language) and associated preconditions, where CAPE improves the correctness metric of the executed task plans by 76.49% compared to SayCan. Our approach enables the robot to follow natural language commands and robustly recover from failures, which baseline approaches largely cannot resolve or address inefficiently. |
| title | CAPE: Corrective Actions from Precondition Errors using Large Language Models |
| topic | Artificial Intelligence Computation and Language Machine Learning Robotics 68T20, 68T50 I.2.7; I.2.8; I.2.2; I.2.4 |
| url | https://arxiv.org/abs/2211.09935 |