Many human activities, given their intrinsic modularity, present structural information which can be exploited by classification algorithms: this enhances the capability of robots to predict activities. We introduce a semantic reasoning paradigm in which, via logical and statistical learning, we discriminate between actions on the basis of contextual associations. An example of this is considering the co-occurrence of scenario objects when predicting an action. We also combine such probabilistic reasoning with traditional sequence likelihood modeling. The system, given partial execution evidence of a task (e.g. assembling a car), first reasons in logical terms over qualitative primitives to constrain the space of possibilities, and then predicts the most sequentially likely action (e.g. 'PickAnd-PutScrew'). A further claim is also the representation of actions in tractable logic, enabling online-capable recognition. Our evaluation, adopting annotated primitives of motion and tool usage, proves that simple sequence-only prediction methods (i.e. bigram sequence information, 59.80%) are outperformed by the proposed polynomial-time context- and sequence-aware inference (i.e. with 8 primitives, various degrees of partial evidence and bigram sequence information, 78.43%), proving the effectiveness of the combined approach.

Online prediction of activities with structure: Exploiting contextual associations and sequences / Kirk, N. H.; Ramirez-Amaro, K.; Dean-Leon, E.; Saveriano, M.; Cheng, G.. - 2015-:(2015), pp. 744-749. (Intervento presentato al convegno 15th IEEE RAS International Conference on Humanoid Robots, Humanoids 2015 tenutosi a International Cooperation Building, Korea Institute of Science and Technology (KIST), Hwarangno 14-gil 5, Seongbuk-gu, kor nel 2015) [10.1109/HUMANOIDS.2015.7363453].

Online prediction of activities with structure: Exploiting contextual associations and sequences

Saveriano M.;
2015-01-01

Abstract

Many human activities, given their intrinsic modularity, present structural information which can be exploited by classification algorithms: this enhances the capability of robots to predict activities. We introduce a semantic reasoning paradigm in which, via logical and statistical learning, we discriminate between actions on the basis of contextual associations. An example of this is considering the co-occurrence of scenario objects when predicting an action. We also combine such probabilistic reasoning with traditional sequence likelihood modeling. The system, given partial execution evidence of a task (e.g. assembling a car), first reasons in logical terms over qualitative primitives to constrain the space of possibilities, and then predicts the most sequentially likely action (e.g. 'PickAnd-PutScrew'). A further claim is also the representation of actions in tractable logic, enabling online-capable recognition. Our evaluation, adopting annotated primitives of motion and tool usage, proves that simple sequence-only prediction methods (i.e. bigram sequence information, 59.80%) are outperformed by the proposed polynomial-time context- and sequence-aware inference (i.e. with 8 primitives, various degrees of partial evidence and bigram sequence information, 78.43%), proving the effectiveness of the combined approach.
2015
IEEE-RAS International Conference on Humanoid Robots
Piscataway, New Jersey, USA
IEEE Computer Society
978-1-4799-6885-5
Kirk, N. H.; Ramirez-Amaro, K.; Dean-Leon, E.; Saveriano, M.; Cheng, G.
Online prediction of activities with structure: Exploiting contextual associations and sequences / Kirk, N. H.; Ramirez-Amaro, K.; Dean-Leon, E.; Saveriano, M.; Cheng, G.. - 2015-:(2015), pp. 744-749. (Intervento presentato al convegno 15th IEEE RAS International Conference on Humanoid Robots, Humanoids 2015 tenutosi a International Cooperation Building, Korea Institute of Science and Technology (KIST), Hwarangno 14-gil 5, Seongbuk-gu, kor nel 2015) [10.1109/HUMANOIDS.2015.7363453].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/331049
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
social impact