Intent Understanding Using an Activation Spreading Architecture

Robotics

Mohammad Taghi Saffar¹  Mircea Nicolescu²  Monica Nicolescu²  Banafsheh Rekabdar²  Nicola Bellotto²  Nick Hawes²  Mohan Sridharan² 

[1] Computer Science and Engineering Department, University of Nevada Reno, Reno, NV 89557, USA;

关键词: intent recognition;    activation spreading network;    activity recognition;    scene understanding;   

DOI  :  10.3390/robotics4030284

来源: mdpi

【 摘 要 】

In this paper, we propose a new approach for recognizing intentions of humans by observing their activities with a color plus depth (RGB-D) camera. Activities and goals are modeled as a distributed network of inter-connected nodes in an Activation Spreading Network (ASN). Inspired by a formalism in hierarchical task networks, the structure of the network captures the hierarchical relationship between high-level goals and low-level activities that realize these goals. Our approach can detect intentions before they are realized and it can work in real-time. We also extend the formalism of ASNs to incorporate contextual information into intent recognition. We further augment the ASN formalism with special nodes and synaptic connections to model ordering constraints between actions, in order to represent and handle partial-order plans in our ASN. A fully functioning system is developed for experimental evaluation. We implemented a robotic system that uses our intent recognition to naturally interact with the user. Our ASN based intent recognizer is tested against three different scenarios involving everyday activities performed by a subject, and our results show that the proposed approach is able to detect low-level activities and recognize high-level intentions effectively in real-time. Further analysis shows that contextual and partial-order ASNs are able to discriminate between otherwise ambiguous goals.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.