Natural language annotations and manuals can provide useful procedural information and relations for the highly specialized scenario of autonomous robotic task planning. In this paper, we propose and publicly release AUTOMATE, a pipeline for automatic task knowledge extraction from expert-written domain texts. AUTOMATE integrates semantic sentence classifcation, semantic role labeling, and identifcation of procedural connectors, in order to extract templates of Linear Temporal Logic (LTL) relations that can be directly implemented in any sufciently expressive logic programming formalism for autonomous reasoning, assuming some low-level commonsense and domain-independent knowledge is available. This is the frst work that bridges natural language descriptions of complex LTL relations and the automation of full robotic tasks. Unlike most recent similar works that assume strict language constraints in substantially simplifed domains, we test our pipeline on texts that refect the expressiveness of natural language used in available textbooks and manuals. In fact, we test AUTOMATE in the surgical robotic scenario, defning realistic language constraints based on a publicly available dataset. In the context of two benchmark training tasks with texts constrained as above, we show that automatically extracted LTL templates, after translation to a suitable logic programming paradigm, achieve comparable planning success in reduced time, with respect to logic programs written by expert programmers
Mapping natural language procedures descriptions to linear temporal logic templates: an application in the surgical robotic domain
Marco Bombieri
;Daniele Meli;Diego Dall’Alba;Marco Rospocher;Paolo Fiorini
2023-01-01
Abstract
Natural language annotations and manuals can provide useful procedural information and relations for the highly specialized scenario of autonomous robotic task planning. In this paper, we propose and publicly release AUTOMATE, a pipeline for automatic task knowledge extraction from expert-written domain texts. AUTOMATE integrates semantic sentence classifcation, semantic role labeling, and identifcation of procedural connectors, in order to extract templates of Linear Temporal Logic (LTL) relations that can be directly implemented in any sufciently expressive logic programming formalism for autonomous reasoning, assuming some low-level commonsense and domain-independent knowledge is available. This is the frst work that bridges natural language descriptions of complex LTL relations and the automation of full robotic tasks. Unlike most recent similar works that assume strict language constraints in substantially simplifed domains, we test our pipeline on texts that refect the expressiveness of natural language used in available textbooks and manuals. In fact, we test AUTOMATE in the surgical robotic scenario, defning realistic language constraints based on a publicly available dataset. In the context of two benchmark training tasks with texts constrained as above, we show that automatically extracted LTL templates, after translation to a suitable logic programming paradigm, achieve comparable planning success in reduced time, with respect to logic programs written by expert programmersFile | Dimensione | Formato | |
---|---|---|---|
Bombieri_et_al_NLP_LTL_2023.pdf
accesso aperto
Tipologia:
Documento in Post-print
Licenza:
Creative commons
Dimensione
1.28 MB
Formato
Adobe PDF
|
1.28 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.